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    "# Free energy calculations\n",
    "\n",
    "In this tutorial we will demonstrate how one can compute the free energy of solids accounting for the full anharmonicity using thermodynamic integration (TI).\n",
    "For a comprehensive discussion of the subject see reference 1 and in particular the excellent article (reference 2) by Freitas *et al.*\n",
    "\n",
    "## Introduction\n",
    "\n",
    "### Thermodynamic integration\n",
    "\n",
    "Consider a Hamiltonian $H(\\lambda)$ that is parametrically dependent on a parameter $\\lambda$.\n",
    "One can show that the free energy difference between two thermodynamic states corresponding to $\\lambda_i$ and $\\lambda_f$ can be computed as\n",
    "$$\n",
    "\\Delta F\n",
    "= F(\\lambda_f) - F(\\lambda_i)\n",
    "= \\int_{\\lambda_i}^{\\lambda_f} \\left < \\frac{\\mathrm{d}H}{\\mathrm{d}\\lambda} \\right >_\\lambda \\mathrm{d}\\lambda.\n",
    "$$\n",
    "The integral can be interpreted as the reversible work done along the path from $\\lambda_i$ to $\\lambda_f$,\n",
    "$$\n",
    "\\Delta F \\equiv W_{i\\rightarrow f}^\\mathrm{rev}.\n",
    "$$\n",
    "In **equilibrium TI approaches** this integral is discretized and one carries out separate simulations for each value of $\\lambda$.\n",
    "\n",
    "In practice **non-equilibrium approaches**, in which $\\lambda$ is updated continuously throughout a simulation, are, however, usually (much) more efficient and we also adopt this approach here.\n",
    "In this case, one computes the *irreversible* work down along a path,\n",
    "$$\n",
    "W_{i\\rightarrow f}^\\mathrm{irr}\n",
    "= \\int_0^{t_s} \\frac{d\\lambda}{dt} \\left(\\frac{dH}{d\\lambda}\\right)_{\\Gamma(t)},\n",
    "$$\n",
    "where $\\lambda=\\lambda(t)$ is a function of the simulation time $t$ and $\\Gamma(t)$ is the phase-space trajectory traversed during the simulation.\n",
    "\n",
    "This basic principle can be employed in several different ways to obtain free energy differences and, given a suitable reference state with a known free energy, ultimately also *total* free energies.\n",
    "Here, we consider\n",
    "* the Frenkel-Ladd approach, in which the Hamiltonian is gradually switched from one Hamiltonian (e.g., an Einstein crystal) to another Hamiltonian (e.g., the system of interest) and\n",
    "* the reversible scaling (RS) approach, in which one integrates the work done as thermodynamic variable (typically the temperature) changes.\n",
    "\n",
    "\n",
    "### Frenkel-Ladd approach\n",
    "\n",
    "We consider two Hamiltonians (interatomic potentials), the first one $H_A$ is the interatomic potential of interest (in this example a NEP model), and the second one $H_B$ is a reference potential for which the free energy is analytically known.\n",
    "We define a mixing Hamiltonian\n",
    "$$\n",
    "H(\\lambda) = (1 - \\lambda) H_A + \\lambda H_B\n",
    "$$\n",
    "where $\\lambda$ is the mixing parameter, such that if $\\lambda=0$ we recover $H_A$ and if $\\lambda=1$ we obtain $H_B$.\n",
    "\n",
    "The free energy differences between the system represented by $H_A$ and $H_B$ can be otained by integration as follows\n",
    "$$\n",
    "F_B - F_A\n",
    "= \\int_0^1 \\left < \\frac{\\mathrm{d}H(\\lambda)}{\\mathrm{d}\\lambda} \\right >_H \\mathrm{d}\\lambda\n",
    "= \\int_0^1 \\left < H_B - H_A \\right >_H \\mathrm{d}\\lambda,\n",
    "$$\n",
    "where $\\left < H_B - H_A \\right >_H$ is the ensemble average of the energy difference between potential $B$ and $A$.\n",
    "\n",
    "Typically it is efficient to sample the integral in the expression above via a molecular dynamics (MD) simulation, during which $\\lambda$ is continously changed between 0 and 1. \n",
    "The change in $\\lambda$ can be carried out in both directions, i.e., with $\\lambda$ changing from $0 \\to 1$ or from $1 \\to 0$, which are referred to as \"forward\" and \"backward\" switching, respectively.\n",
    "This type of integration typically incurs some a systematic error due to the simulation not being in equilibrium due to $\\lambda$ changing continously in the simulation.\n",
    "However, these systematic errors are the same but with opposite sign in forward and backward integration and hence the average over these two lead to a much more accurate free energy [2],\n",
    "$$\n",
    "F_\\mathrm{average} = \\left( F_\\mathrm{forward} + F_\\mathrm{backward} \\right) / 2.\n",
    "$$\n",
    "\n",
    "Alternatively one can also run multiple runs with fixed values of $\\lambda$, but this is typically much less efficient.\n",
    "\n",
    "In order for the thermodynamic integration to converge and be as accurate as possible the reference Halmitonian should be chosen as \"similar\" to the Hamiltonian of the system of interest as possible.\n",
    "This means that the phase space sampled by the reference potential at the given temperature should be as close as possible to the phase space sampled by the real potential.\n",
    "\n",
    "A good and common choice of reference system is an **Einstein crystal**, where each atom is tethered to a reference position with a harmonic spring, i.e.,\n",
    "$$\n",
    "U = \\sum_i k (r_i - r_i^0)^2,\n",
    "$$\n",
    "where $k$ is the spring constant (corresponding to a frequency $\\omega_\\mathrm{E}$), $r_i$ is the position of atom $i$ and $r_i^0$ is the reference position.\n",
    "A good choice for the spring constant is such that the mean square displacements of the Einstein crystal is similar to that of the reference system.\n",
    "Or it can be chosen such that the frequency of the Einstein solid is similar to a typical/average phonon frequency in the system.\n",
    "\n",
    "The free energy of the Einstein crystal is given by\n",
    "$$\n",
    "F_\\mathrm{E} = 3 N k_\\mathrm{B} T \\ln{\\left(\\frac{\\hbar \\omega_\\mathrm{E}}{k_\\mathrm{B}T} \\right )}.\n",
    "$$\n",
    "Note that since we are running classical MD simulations we need to use the classical harmonic free energy and not the quantum variant.\n",
    "\n",
    "There is a small correction due to the center of mass being fixed in MD simulations and thus effectivley, three degrees of freedoms are not sampled [2].\n",
    "This is, however, a very small effect and usually (i) cancels out when considering free energy difference between two phases and (ii) vanishes in the thermodynamic limit, i.e., for large system sizes.\n",
    "\n",
    "Note that if your system exhibits any kind of diffusion, an Einstein crystal will be a poor reference.\n",
    "\n",
    "### Reversible scaling\n",
    "\n",
    "In the reversible scaling (RS) approach each value of $\\lambda$ corresponds to a different temperature, and one considers the scaled Hamiltonian\n",
    "$$\n",
    "H(\\lambda) = \\sum_i^\\mathrm{atoms} \\frac{\\vec{p}_i^2}{2 m_i} + \\lambda U(\\vec{r}).\n",
    "$$\n",
    "In practice one carries out a simulation during which $\\lambda$ varies with time.\n",
    "Afterwards one calculates the irreversible work \n",
    "$$\n",
    "W_{1\\rightarrow\\lambda_f}^\\mathrm{irr}\n",
    "= \\int_0^{t_s} dt \\frac{d\\lambda}{dt} U(\\Gamma(t))\n",
    "$$\n",
    "and then obtains the free energy as a (continuous) function of temperature via\n",
    "$$\n",
    "F_0(T) = \\frac{F(T_0)}{\\lambda}\n",
    "+ \\frac{3}{2} N k_\\mathrm{B} T_0 \\frac{\\ln\\lambda}{\\lambda}\n",
    "+ \\frac{1}{2\\lambda} \\left[\n",
    "W_{1\\rightarrow\\lambda}^\\mathrm{irr} - W_{\\lambda\\rightarrow 1}^\\mathrm{irr}\n",
    "\\right]\n",
    "$$\n",
    "\n",
    "### Application case\n",
    "\n",
    "In this tutorial we will compute the free energy of titanium in two phases, body-centered cubic (BCC) and hexagonal close-packed HCP using a NEP potential.\n",
    "Titanium undergoes a phase transition from HCP to BCC at around 1150 K experimentally.\n",
    "Here we will use the universal NEP from [5] provided in the file `nep-unep-v1.txt`.\n",
    "\n",
    "Here we only consider a single set of parameters for system size, simulation length, and, in the case of the Frenkel-Ladd approach, the value of the spring constant.\n",
    "In general one should, however, carefully check the convergence of the results with respect to these parameters.\n",
    "\n",
    "\n",
    "### References\n",
    "\n",
    "For more detailed information see, e.g.,\n",
    "* [1] *Understanding Molecular Simulation* by D. Frenkel and B. Smit\n",
    "* [2] *Nonequilibrium free-energy calculation of solids using LAMMPS*, Freitas *et al.*, [Computational Materials Science 112, 333 (2016)](https://doi.org/10.1016/j.commatsci.2015.10.050)\n",
    "* [3] https://en.wikipedia.org/wiki/Thermodynamic_integration\n",
    "* [4] GPUMD documentation, https://gpumd.org/gpumd/input_parameters/ensemble_ti_spring.html\n",
    "* [5] *General-purpose machine-learned potential for 16 elemental metals and their alloys*, Song *et al.*, [arxiv.2311.04732 (2024)](https://doi.org/10.48550/arXiv.2311.04732); [zenodo record with model](https://zenodo.org/doi/10.5281/zenodo.10081676)\n"
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    }
   },
   "cell_type": "markdown",
   "id": "e7b98f72-7ff5-4c85-a2be-65535fc6be54",
   "metadata": {},
   "source": [
    "## Frenkel-Ladd approach\n",
    "\n",
    "### Thermal expansion\n",
    "\n",
    "The switching simulations with respect to the Einstein crystal are carried out in an NVT ensemble.\n",
    "Therefore we need to know the temperature dependence of the lattice parameters beforehand.\n",
    "This information can be readily obtained through NPT simulations.\n",
    "Here, we use a simple polynomial to the data show below.\n",
    "\n",
    "<img src=\"attachment:fae5c119-35d1-4a13-91cf-555348b50c73.png\" width=\"800\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d2155e0-1db0-4b00-a196-42ed33c3e3ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Ti_bcc_lattice_parameter(T):\n",
    "    alat = 3.2700559351528953 + 1.311174893837472e-05 * T + 6.272944257392548e-09 * T ** 2\n",
    "    return alat\n",
    "\n",
    "\n",
    "def get_Ti_hcp_lattice_parameter(T):\n",
    "    alat = 2.9337472912371387 + 2.9726486008937908e-05 * T + -7.026081914635316e-09 * T ** 2\n",
    "    clat = 4.655766212690983 + 2.8534727442310983e-05 * T + 2.2462470604534456e-08 * T ** 2\n",
    "    return alat, clat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f43ff8-b8e6-4166-9049-757307a260bb",
   "metadata": {},
   "source": [
    "### Setting up the runs\n",
    "\n",
    "The switching is carried out by first equilibrating for `n_eq` number of time steps, then swithcing forward for `n_sample` steps, followed by equilibrating again and then switching backwards.\n",
    "The total simulation time is thus `n_total = 2 * (n_eq + n_sample)`.\n",
    "\n",
    "The simulation cell is chosen to be as close to a cubic shape as possible in order to maximize the distance between periodic images.\n",
    "\n",
    "See the [documentation of the ti_spring command](https://gpumd.org/gpumd/input_parameters/ensemble_ti_spring.html) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34b2bc8c-6069-4967-b4a2-8c7880b3ed8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# MD parameters\n",
    "temperatures = np.arange(700, 1401, 50)\n",
    "\n",
    "dt = 2.0  # timestep in fs\n",
    "T_damp = 100  # thermostat damping\n",
    "thermo_interval = 100   # thermo.out write interval\n",
    "spring_k = 2.0   # spring constant\n",
    "\n",
    "potential_fname = 'nep-unep-v1.txt'\n",
    "\n",
    "# number of timesteps to run\n",
    "n_eq = 20 * 1000\n",
    "n_sample = 100 * 1000\n",
    "n_total = 2 * (n_eq + n_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e249b1bf-dfc2-4919-833d-9d3084f7add5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from ase.build import bulk\n",
    "from calorine.gpumd.io import write_xyz\n",
    "\n",
    "# set up MD run directories and input files\n",
    "for phase in ['bcc', 'hcp']:\n",
    "    for T in temperatures:\n",
    "\n",
    "        # set up structure\n",
    "        if phase == 'bcc':\n",
    "            alat = get_Ti_bcc_lattice_parameter(T)\n",
    "            prim = bulk('Ti', crystalstructure='bcc', a=alat, cubic=True)\n",
    "            size = 20\n",
    "        elif phase == 'hcp':\n",
    "            alat, clat = get_Ti_hcp_lattice_parameter(T)\n",
    "            prim = bulk('Ti', crystalstructure='hcp', a=alat, c=clat, orthorhombic=True)\n",
    "            size = [20, 12, 13]\n",
    "        supercell = prim.repeat(size)\n",
    "    \n",
    "        # set up directory\n",
    "        dirname = f'my_freen_runs/switch_{phase}_T{T}_nat{len(supercell)}' \\\n",
    "                  f'_nsample{n_sample}_spring{spring_k:5f}'\n",
    "        os.makedirs(dirname, exist_ok=True)\n",
    "    \n",
    "        # write model.xyz\n",
    "        write_xyz(os.path.join(dirname, 'model.xyz'), supercell)\n",
    "    \n",
    "        # write run.in\n",
    "        with open(os.path.join(dirname, 'run.in'), 'w') as f:\n",
    "            f.write(f'potential     {os.path.abspath(potential_fname)}\\n')\n",
    "            # write equilibration\n",
    "            f.write(f'velocity      {1.0 * T}\\n')\n",
    "            f.write(f'ensemble      nvt_lan {T} {T} {T_damp}\\n')\n",
    "            f.write(f'time_step     {dt}\\n')\n",
    "            f.write(f'run           {n_eq}\\n\\n\\n')\n",
    "        \n",
    "            # write production\n",
    "            f.write(f'ensemble      ti_spring temp {T} tperiod {T_damp}'\n",
    "                    f' tequil {n_eq} tswitch {n_sample} spring Ti {spring_k}\\n')\n",
    "            f.write(f'time_step     {dt}\\n')\n",
    "            f.write(f'dump_thermo   {thermo_interval}\\n')\n",
    "            f.write(f'run           {n_total}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "787fe59e-9d19-41e3-a615-b4c1024caeda",
   "metadata": {},
   "source": [
    "After these simulations have been run (outside this notebook), we can collect the data and extract the free energies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "665698ae-7353-4f44-a610-13f8ae4e9294",
   "metadata": {},
   "source": [
    "### Inspecting a single run\n",
    "First we carry out the integration \"manually\" by using the data in `ti_spring.csv` to make sure it works as expected for one simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2bb068bc-82ba-4c66-8ff0-2e1b44035c52",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>lambda</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.310780</td>\n",
       "      <td>0.130732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.310513</td>\n",
       "      <td>0.130725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-2.273464e-18</td>\n",
       "      <td>-7.310265</td>\n",
       "      <td>0.130720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-2.273555e-18</td>\n",
       "      <td>-7.310057</td>\n",
       "      <td>0.130710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.309881</td>\n",
       "      <td>0.130692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             lambda       dlambda        pe   espring\n",
       "0      0.000000e+00  0.000000e+00 -7.649767  0.340141\n",
       "1      1.259958e-23  6.299748e-23 -7.649792  0.340090\n",
       "2      4.031731e-22  1.007919e-21 -7.649821  0.340028\n",
       "3      3.061494e-21  5.102388e-21 -7.649841  0.339962\n",
       "4      1.290068e-20  1.612542e-20 -7.649838  0.339873\n",
       "...             ...           ...       ...       ...\n",
       "99995  1.000000e+00  0.000000e+00 -7.310780  0.130732\n",
       "99996  1.000000e+00  0.000000e+00 -7.310513  0.130725\n",
       "99997  1.000000e+00 -2.273464e-18 -7.310265  0.130720\n",
       "99998  1.000000e+00 -2.273555e-18 -7.310057  0.130710\n",
       "99999  1.000000e+00  0.000000e+00 -7.309881  0.130692\n",
       "\n",
       "[100000 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from ase.io import read\n",
    "\n",
    "T = 1000\n",
    "dirname = f'my_freen_runs/switch_bcc_T{T}_nat16000_nsample100000_spring2.000000'\n",
    "\n",
    "# read data\n",
    "df = pd.read_csv(f'{dirname}/ti_spring.csv')\n",
    "atoms_ideal = read(f'{dirname}/model.xyz')\n",
    "n_atoms = len(atoms_ideal)\n",
    "\n",
    "# free energy from backward and forward switching\n",
    "n2 = int(len(df) / 2)\n",
    "df_fwd, df_bwd = df[:n2], df[n2:]\n",
    "display(df_fwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92b5371e-ad50-4aaf-8ae2-b3bf2f6703c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(4.0, 2.5), dpi=200)\n",
    "\n",
    "ax.plot(df_fwd['lambda'], (df_fwd.pe - df_fwd.espring),\n",
    "        '-', lw=3.0, alpha=0.5, label='forward')\n",
    "ax.plot(df_bwd['lambda'], (df_bwd.pe - df_bwd.espring),\n",
    "        '-', lw=1.5, alpha=0.5,  label='backward')\n",
    "\n",
    "ax.set_ylabel('$U_\\mathrm{NEP} - U_\\mathrm{Einstein}$ (eV/atom)')\n",
    "ax.set_xlabel('$\\lambda$')\n",
    "ax.set_xlim(0, 1)\n",
    "ax.legend(frameon=False)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174d6fc3-e941-440c-8719-509db98390fa",
   "metadata": {},
   "source": [
    "We can now obtain the free energy by integrating (or rather summing) over $\\lambda$.\n",
    "By averaging over the forward and backward switching runs the systematic error due to the finite switching rate cancels.\n",
    "\n",
    "The free energy obtained in this fashion is also directly available from the `yaml` output file.\n",
    "And indeed the `Free energy (average)` value agrees that we obtained by \"manually\" integrating the data from the `csv` file agrees with the value for `F` given in the `yaml` output file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eec95986-eb29-407d-bf5f-d22e282296c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Free energy (forward)  :   -8.2076525 eV/atom\n",
      "Free energy (backward) :   -8.2072549 eV/atom\n",
      "Free energy (average)  :   -8.2074537 eV/atom\n",
      "\n",
      "E_Einstein             :   -0.4847130\n",
      "E_diff                 :   -7.7227420\n",
      "F                      :   -8.2074550\n",
      "T                      : 1000.0000000\n",
      "V                      :   17.7965640\n",
      "P                      :    0.0000000\n",
      "G                      :   -8.2074550\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "from ase.units import _amu as amu, kB, _e as eV, _hbar as hbar\n",
    "\n",
    "# work done by switching \n",
    "work_switch_fwd = np.sum(df_fwd.dlambda * (df_fwd.pe - df_fwd.espring))\n",
    "work_switch_bwd = np.sum(df_bwd.dlambda * (df_bwd.espring - df_bwd.pe))\n",
    "work_switch_ave = (work_switch_fwd + work_switch_bwd) / 2\n",
    "\n",
    "# define harmonic reference system free energy\n",
    "mass = atoms_ideal.get_masses()#[0]\n",
    "omegas = np.sqrt(spring_k * eV / (mass * amu)) * 1e10  # in 1/s\n",
    "F_oscillators = 3 * kB * T * np.log(hbar / eV * omegas / (kB * T))  # in eV\n",
    "F_einstein = np.sum(F_oscillators) / n_atoms\n",
    "\n",
    "# free energies for NEP model\n",
    "F_fwd = work_switch_fwd + F_einstein\n",
    "F_bwd = work_switch_bwd + F_einstein\n",
    "F_avg = work_switch_ave + F_einstein\n",
    "\n",
    "print(f'Free energy (forward)  : {F_fwd:12.7f} eV/atom')\n",
    "print(f'Free energy (backward) : {F_bwd:12.7f} eV/atom')\n",
    "print(f'Free energy (average)  : {F_avg:12.7f} eV/atom')\n",
    "print('')\n",
    "\n",
    "# compare to values in ti_spring.yaml\n",
    "with open(f'{dirname}/ti_spring.yaml') as f:\n",
    "    row = yaml.load(f, Loader=yaml.FullLoader)\n",
    "\n",
    "for key, val in row.items():\n",
    "    print(f'{key:22} : {val:12.7f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0a4929-8bcb-457a-a872-70c78042b8ce",
   "metadata": {},
   "source": [
    "### Temperature dependence\n",
    "\n",
    "Next, we analyze the free energy as a function of temperature for both BCC and HCP phases by parsing the `yaml` files from a series of runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8732ce20-01a8-46af-926d-c12c7b04a31e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bcc\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E_Einstein</th>\n",
       "      <th>E_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>T</th>\n",
       "      <th>V</th>\n",
       "      <th>P</th>\n",
       "      <th>G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.274753</td>\n",
       "      <td>-7.738216</td>\n",
       "      <td>-8.012969</td>\n",
       "      <td>700.0</td>\n",
       "      <td>17.681046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.012969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.307756</td>\n",
       "      <td>-7.735010</td>\n",
       "      <td>-8.042766</td>\n",
       "      <td>750.0</td>\n",
       "      <td>17.698996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.042766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.341620</td>\n",
       "      <td>-7.731752</td>\n",
       "      <td>-8.073372</td>\n",
       "      <td>800.0</td>\n",
       "      <td>17.717466</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.073372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.376293</td>\n",
       "      <td>-7.728475</td>\n",
       "      <td>-8.104768</td>\n",
       "      <td>850.0</td>\n",
       "      <td>17.736457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.104768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.411727</td>\n",
       "      <td>-7.725250</td>\n",
       "      <td>-8.136977</td>\n",
       "      <td>900.0</td>\n",
       "      <td>17.755969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.136977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.447880</td>\n",
       "      <td>-7.722060</td>\n",
       "      <td>-8.169939</td>\n",
       "      <td>950.0</td>\n",
       "      <td>17.776004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.169939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.484713</td>\n",
       "      <td>-7.718797</td>\n",
       "      <td>-8.203510</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>17.796564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.203510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.522192</td>\n",
       "      <td>-7.715521</td>\n",
       "      <td>-8.237713</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>17.817649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.237713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.560287</td>\n",
       "      <td>-7.712365</td>\n",
       "      <td>-8.272653</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>17.839260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.272653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.598970</td>\n",
       "      <td>-7.709060</td>\n",
       "      <td>-8.308030</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>17.861399</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.308030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.638216</td>\n",
       "      <td>-7.705951</td>\n",
       "      <td>-8.344166</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>17.884068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.344166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.678000</td>\n",
       "      <td>-7.702832</td>\n",
       "      <td>-8.380831</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>17.907266</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.380831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.718301</td>\n",
       "      <td>-7.699630</td>\n",
       "      <td>-8.417930</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>17.930996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.417930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.759099</td>\n",
       "      <td>-7.696553</td>\n",
       "      <td>-8.455652</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>17.955259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.800376</td>\n",
       "      <td>-7.693403</td>\n",
       "      <td>-8.493780</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>17.980057</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.493780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    E_Einstein    E_diff         F       T          V    P         G\n",
       "0    -0.274753 -7.738216 -8.012969   700.0  17.681046  0.0 -8.012969\n",
       "1    -0.307756 -7.735010 -8.042766   750.0  17.698996  0.0 -8.042766\n",
       "2    -0.341620 -7.731752 -8.073372   800.0  17.717466  0.0 -8.073372\n",
       "3    -0.376293 -7.728475 -8.104768   850.0  17.736457  0.0 -8.104768\n",
       "4    -0.411727 -7.725250 -8.136977   900.0  17.755969  0.0 -8.136977\n",
       "5    -0.447880 -7.722060 -8.169939   950.0  17.776004  0.0 -8.169939\n",
       "6    -0.484713 -7.718797 -8.203510  1000.0  17.796564  0.0 -8.203510\n",
       "7    -0.522192 -7.715521 -8.237713  1050.0  17.817649  0.0 -8.237713\n",
       "8    -0.560287 -7.712365 -8.272653  1100.0  17.839260  0.0 -8.272653\n",
       "9    -0.598970 -7.709060 -8.308030  1150.0  17.861399  0.0 -8.308030\n",
       "10   -0.638216 -7.705951 -8.344166  1200.0  17.884068  0.0 -8.344166\n",
       "11   -0.678000 -7.702832 -8.380831  1250.0  17.907266  0.0 -8.380831\n",
       "12   -0.718301 -7.699630 -8.417930  1300.0  17.930996  0.0 -8.417930\n",
       "13   -0.759099 -7.696553 -8.455652  1350.0  17.955259  0.0 -8.455652\n",
       "14   -0.800376 -7.693403 -8.493780  1400.0  17.980057  0.0 -8.493780"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hcp\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E_Einstein</th>\n",
       "      <th>E_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>T</th>\n",
       "      <th>V</th>\n",
       "      <th>P</th>\n",
       "      <th>G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.274753</td>\n",
       "      <td>-7.751881</td>\n",
       "      <td>-8.026634</td>\n",
       "      <td>700.0</td>\n",
       "      <td>17.674366</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.026634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.307756</td>\n",
       "      <td>-7.746786</td>\n",
       "      <td>-8.054542</td>\n",
       "      <td>750.0</td>\n",
       "      <td>17.697600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.054542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.341620</td>\n",
       "      <td>-7.741753</td>\n",
       "      <td>-8.083373</td>\n",
       "      <td>800.0</td>\n",
       "      <td>17.720854</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.083373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.376293</td>\n",
       "      <td>-7.736778</td>\n",
       "      <td>-8.113071</td>\n",
       "      <td>850.0</td>\n",
       "      <td>17.744130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.113071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.411727</td>\n",
       "      <td>-7.731945</td>\n",
       "      <td>-8.143672</td>\n",
       "      <td>900.0</td>\n",
       "      <td>17.767427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.143672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.447880</td>\n",
       "      <td>-7.727120</td>\n",
       "      <td>-8.175000</td>\n",
       "      <td>950.0</td>\n",
       "      <td>17.790744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.484713</td>\n",
       "      <td>-7.722391</td>\n",
       "      <td>-8.207104</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>17.814080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.207104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.522192</td>\n",
       "      <td>-7.717811</td>\n",
       "      <td>-8.240003</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>17.837436</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.240003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.560287</td>\n",
       "      <td>-7.713271</td>\n",
       "      <td>-8.273558</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>17.860809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.273558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.598970</td>\n",
       "      <td>-7.708817</td>\n",
       "      <td>-8.307787</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>17.884201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.307787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.638216</td>\n",
       "      <td>-7.704417</td>\n",
       "      <td>-8.342633</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>17.907609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.342633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.678000</td>\n",
       "      <td>-7.700044</td>\n",
       "      <td>-8.378044</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>17.931033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.378044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.718301</td>\n",
       "      <td>-7.695847</td>\n",
       "      <td>-8.414148</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>17.954472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.414148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.759099</td>\n",
       "      <td>-7.691721</td>\n",
       "      <td>-8.450821</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>17.977925</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.450821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.800376</td>\n",
       "      <td>-7.687757</td>\n",
       "      <td>-8.488134</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>18.001392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.488134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    E_Einstein    E_diff         F       T          V    P         G\n",
       "0    -0.274753 -7.751881 -8.026634   700.0  17.674366  0.0 -8.026634\n",
       "1    -0.307756 -7.746786 -8.054542   750.0  17.697600  0.0 -8.054542\n",
       "2    -0.341620 -7.741753 -8.083373   800.0  17.720854  0.0 -8.083373\n",
       "3    -0.376293 -7.736778 -8.113071   850.0  17.744130  0.0 -8.113071\n",
       "4    -0.411727 -7.731945 -8.143672   900.0  17.767427  0.0 -8.143672\n",
       "5    -0.447880 -7.727120 -8.175000   950.0  17.790744  0.0 -8.175000\n",
       "6    -0.484713 -7.722391 -8.207104  1000.0  17.814080  0.0 -8.207104\n",
       "7    -0.522192 -7.717811 -8.240003  1050.0  17.837436  0.0 -8.240003\n",
       "8    -0.560287 -7.713271 -8.273558  1100.0  17.860809  0.0 -8.273558\n",
       "9    -0.598970 -7.708817 -8.307787  1150.0  17.884201  0.0 -8.307787\n",
       "10   -0.638216 -7.704417 -8.342633  1200.0  17.907609  0.0 -8.342633\n",
       "11   -0.678000 -7.700044 -8.378044  1250.0  17.931033  0.0 -8.378044\n",
       "12   -0.718301 -7.695847 -8.414148  1300.0  17.954472  0.0 -8.414148\n",
       "13   -0.759099 -7.691721 -8.450821  1350.0  17.977925  0.0 -8.450821\n",
       "14   -0.800376 -7.687757 -8.488134  1400.0  18.001392  0.0 -8.488134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from glob import glob\n",
    "\n",
    "dfs_spring = dict()\n",
    "for phase in ['bcc', 'hcp']:\n",
    "    records = []\n",
    "    for fname in glob(f'my_freen_runs/switch_{phase}*/ti_spring.yaml'):\n",
    "        with open(fname) as f:\n",
    "            row = yaml.load(f, Loader=yaml.FullLoader)\n",
    "        records.append(row)\n",
    "    df = pd.DataFrame(records).sort_values('T', ignore_index=True)\n",
    "    dfs_spring[phase] = df\n",
    "    print(phase)\n",
    "    display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a178f51f-ad2b-4ee1-8b41-d38318b58d0f",
   "metadata": {},
   "source": [
    "We can now plot the free energy as a function of temperature which shows that at some temperature between 1000 and 1300 K the BCC phase becomes more stable than the HCP phase.\n",
    "Due to the very similar slopes it is, however, difficult to locate the exact transition temperature.\n",
    "To obtain a clearer picture it is therefore preferable to consider the free energy *difference* $\\Delta F$ between the two phases, which is shown in the bottom panel.\n",
    "This yields a transition temperature of about 1140 K, in good agreement with the experimentally reported value of 1155 K.\n",
    "\n",
    "This analysis incidentally also highlights the biggest challenge in free energy calculations, namely that one the free energy differences must be very well converged in order to control the error in the transition temperature.\n",
    "To obtain accuracte results it is therefore a good idea to run multiple identical independent simulatons, and average the thus obtained free energies to reduce the error.\n",
    "This also allows one provide a standard error estimate for the free energy by computing the standard deviation of all obtained free energies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e94e0bc-623c-4dc0-8dc5-9fbf5454cf97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 680x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(3.4, 3.5), dpi=200, nrows=2, sharex=True)\n",
    "\n",
    "for phase in ['bcc', 'hcp']:\n",
    "    axes[0].plot(dfs_spring[phase]['T'], dfs_spring[phase].F,\n",
    "                 '-o', label=phase, alpha=0.5)\n",
    "\n",
    "ax = axes[1]\n",
    "ax.plot(dfs_spring['bcc']['T'], 1e3 * (dfs_spring['bcc'].F - dfs_spring['hcp'].F),\n",
    "        '-o', alpha=0.5)\n",
    "ax.axvline(1136, color='0.5', alpha=0.5, ls=':')\n",
    "ax.axhline(0, color='0.5', alpha=0.5, ls=':')\n",
    "\n",
    "axes[0].set_ylabel('$F$ (eV/atom)')\n",
    "axes[1].set_ylabel('$\\Delta F$ (meV/atom)')\n",
    "axes[-1].set_xlabel('Temperature (K)')\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.align_ylabels()\n",
    "fig.subplots_adjust(hspace=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b10a49f-411e-4d09-88c3-de5d0c327524",
   "metadata": {},
   "source": [
    "## Reversible scaling approach\n",
    "\n",
    "We now also consider the reversible scaling (RS) approach.\n",
    "In the following cell we set up the input files for two runs, one for BCC and one for HCP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9aeb3e33-69b6-40d0-b260-1e88afdaaa7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_temperature, final_temperature = 700, 1500\n",
    "n_switch = 40000\n",
    "n_equil = 1000\n",
    "\n",
    "for phase in ['bcc', 'hcp']:\n",
    "    # set up structure\n",
    "    if phase == 'bcc':\n",
    "        alat = get_Ti_bcc_lattice_parameter(initial_temperature)\n",
    "        prim = bulk('Ti', crystalstructure='bcc', a=alat, cubic=True)\n",
    "        size = 20\n",
    "    elif phase == 'hcp':\n",
    "        alat, clat = get_Ti_hcp_lattice_parameter(initial_temperature)\n",
    "        prim = bulk('Ti', crystalstructure='hcp', a=alat, c=clat, orthorhombic=True)\n",
    "        size = [20, 12, 13]\n",
    "    supercell = prim.repeat(size)\n",
    "\n",
    "    # set up directory\n",
    "    dirname = f'my_freen_runs/rs_{phase}_Ti{initial_temperature}' \\\n",
    "              f'_Ti{final_temperature}_nat{len(supercell)}_nswitch{n_switch}'\n",
    "    os.makedirs(dirname, exist_ok=True)\n",
    "    \n",
    "    # write model.xyz\n",
    "    write_xyz(os.path.join(dirname, 'model.xyz'), supercell)\n",
    "\n",
    "    # write run.in\n",
    "    with open(os.path.join(dirname, 'run.in'), 'w') as f:\n",
    "        f.write(f'potential     {os.path.abspath(potential_fname)}\\n')\n",
    "        # write equilibration\n",
    "        f.write(f'velocity      {initial_temperature}\\n')\n",
    "        f.write(f'ensemble      nvt_lan {initial_temperature} {initial_temperature} {T_damp}\\n')\n",
    "        f.write(f'time_step     {dt}\\n')\n",
    "        f.write(f'run           {n_eq}\\n\\n\\n')\n",
    "    \n",
    "        # write production\n",
    "        f.write(f'ensemble      ti_rs temp {initial_temperature} {final_temperature}'\n",
    "                f' aniso 0 tswitch {n_switch} tequil {n_equil}\\n')\n",
    "        f.write(f'time_step     {dt}\\n')\n",
    "        f.write(f'dump_thermo   {thermo_interval}\\n')\n",
    "        f.write(f'run           {2 * n_switch + n_equil}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb3d59e-7fb3-4a36-b2df-bbde21419f77",
   "metadata": {},
   "source": [
    "After the two runs have finished we can analyze the results.\n",
    "As apparent from the expression for the total free energy in the RS approach (see above), we need both the variation of the energy (or enthalpy) throughout the simulation and the *total* free energy at the reference (initial) temperature.\n",
    "Here, the latter is taken from the Frenkel-Ladd approach.\n",
    "\n",
    "For convenience we define a function that carries out all the data manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6ba6a19-5232-43af-84b0-8a0e75cce8ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.integrate import cumulative_trapezoid\n",
    "\n",
    "def get_free_energy(\n",
    "    reference_freen_yaml: str,\n",
    "    reversible_switching_csv: str,\n",
    "):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    reference_freen_yaml\n",
    "        Path to ti_spring.yaml file with the free energy\n",
    "        at the reference (initial) temperature.\n",
    "    reversible_switching_csv\n",
    "        Path to ti_rs.csv file with the data from the\n",
    "        reversible scaling run.\n",
    "    \"\"\"\n",
    "\n",
    "    # One requires the total free energy at the initial temperature.\n",
    "    # Here it is assumed to come from a calculation using the ti_spring command.\n",
    "    with open(reference_freen_yaml, 'r') as f:\n",
    "        y =  yaml.safe_load(f)\n",
    "    reference_temperature = y['T']\n",
    "    reference_freen = y['G']\n",
    "\n",
    "    # Read data from reversible scaling (RS) run and split it into forward and backward sequences.\n",
    "    rs = pd.read_csv(reversible_switching_csv)\n",
    "    n = int(len(rs)/2)\n",
    "    forward = rs[:n]\n",
    "    backward = rs[n:][::-1].reset_index()\n",
    "\n",
    "    # Compute the irreversible work done along the lambda-path.\n",
    "    lmb = forward['lambda']\n",
    "    work_fwd = cumulative_trapezoid(forward['enthalpy'], lmb, initial=0)\n",
    "    work_bwd = cumulative_trapezoid(backward['enthalpy'], lmb, initial=0)\n",
    "    work = (work_fwd + work_bwd) / 2\n",
    "\n",
    "    # Compute the free energy.\n",
    "    freen = (reference_freen + 3 / 2 * kB * reference_temperature * np.log(lmb) + work) / lmb\n",
    "    temperature = reference_temperature / lmb\n",
    "\n",
    "    df = pd.DataFrame(np.transpose([temperature, freen]), columns=['temperature', 'free_energy'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d10b43-e27b-4d83-9212-569f3bebcafe",
   "metadata": {},
   "source": [
    "Now we can plot the results, which shows that the results from RS approach are in very good agreement with the Frenkel-Ladd results.\n",
    "The RS approach requires far fewer simulations and thus is often preferable.\n",
    "It does, however, require a reference free energy, which needs to be very accurate in order to obtain reliable results.\n",
    "In practice it can therefore often be beneficial to add reference calculations (using Frenkel-Ladd) also at some higher temperature(s) in order to check the convergence of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3dfd41a-3620-4187-9d47-ccf5ce519980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zhLw6s6oLUIGigGv3Jp++YukcL9a08fq7HUyrrGLW1ImE27ZCdy07rRKWJWfRYXmpNUNEbTeGYhG2o2hqFEv3024FaMoYhHSLMb4si80JLGQX1bFGaK3JT6dLnkohhBAnOAkoxbBSFIUF08pZ/FY9k8uC1DTH2NAQpaLA0zONvYftOHQksjR2pUmZOSaXBSkNebhiThUhr8GGhm5W13YRS++7GSebs1ld28Xb25JMiE6gWknyolJOqxOgJhPAq5pMcnVQqKX2VuqhExuNDr2IRruQjVEvkwMKy6xpXGG8Q2nEzidBl4BSCCHECU4CSjHsSkMeFs6oYNnaRnRNpbY9wdaWOIamEvYaaKqCZeenuU3LJuTRmVoZJuJ3sXBGBaW7p7vnVEeYNbqQra1xVu3spLE7vc+1HNtia9zDkpaZZBUXcSVAuTfJZL0D1dr3eBWLYquFCG1soZKaeADd8LC8exTXOKBY+9ZpF0IIMfief/55Lrjggv2+7vf7qaysZO7cudxwww28733vO6R2a2treeSRR3jmmWfYsmULra2t2LZNJBJh+vTpnHvuuVx99dWMGzfuoG1ls1kef/xxnnrqKVasWEFrayvRaJRwOEx1dTVnnHEGH/3oR3nf+96Hqo7satiKcziZs4XoR319PaNHjwagrq6OqqqqI2qnJZpm+YYm2uJZUtkczbEMiUwOy3bQ1Pxu8LKgG69LpzjgYsG08p5gsj8NXSlW1XaytSVOz/9yM0l0x9tsbLNotXzYqpvRvgyVHpMSLY6WiebLO/bDdmBDphQNm6m+bi4/2cPo868/cBUeIYQYqY7xUrQHCyjf67rrruP+++9H07R+X0+n0/znf/4n9913H5nMe7OD9KUoCldccQU/+tGPej7/3mvJkiXceuut7Nix46B9mzx5Mj/5yU+4+OKLD3rs0Rqoz+z3khFKccwoDXm4Zl419Z0p1tR3sa0lgd3r9x1VUZhYGmBGVZiqQi/KQVL2VBZ4qSzw0p0yebuui/W7usnaLpoyLkwlR8ZSiLgssrbKjqSbesVFmTtImS+By9y9E7wXVYEKLcbWlJ+UmmbtLpPR8vuYEOJ4MkJL0X7hC1/gi1/8Ys/fHceho6OD1157jZ/+9Ke0tLTwhz/8gdGjR3PXXXftc35bWxuLFi3i9ddfByAYDPKJT3yC973vfVRVVWEYBk1NTbzyyissWbKELVu28Oijj3LmmWfyla98ZZ/27rzzTm677baev//bv/0bH/7wh5k6dSoFBQV0dHTwzjvvsHTpUp555hlqamr41re+NSQB5WCRgFIcUxRFYXTEx+iIj0zOIp7OYVoOhqYQ8Oi49f5/szyQsNfg/MklzBsfYXVtJ29vDpAgg4qJlxyQL/GYcxR2pV00Ki6KXQEqfEm8ZtfuwDIfOEaUKAZump1CtiZNMit+h7tyGlSfCd7CgXsjhBBiqI3gUrSlpaVMnz59n6+ff/75fPjDH2bOnDmk02l+9rOfcdttt+FyuXqOsW2bK6+8sieYXLhwIf/3f/9HaWnpPu0tWrSI733vezz00EN87Wtf67cvDzzwQE8wWVpayqOPPsr555+/z3Ef+MAH+NKXvsT69eu55ZZbaG1tPaJ7P1ZIQCmOWW5dwx04/ADyQO1NKg0yc1wZmU3bcdkmipUDzYBev2jbDrRkDFoyYQqNABXeJMFcB0o2gWplCGsZEk4A2+UlbsZxN66BpnVQNhXGnHl4U+DH+JSSEOIE0fHucVuKdurUqVx88cU8/vjjxGIxNm/ezIwZM3pe/5//+R+ee+45ABYsWMBf/vIXdH3/4ZGqqlx77bVccMEF1NTU9Hlt165d/Pu//zuQX8P5wgsvMGXKlAP2b/r06SxfvpyHH374SG/xmCABpTihmJaD4i3A6zIo0VMUm800OCZdFPYJKvfoNDU6zSABzUcFjUSUNJpuYKGDbmDau5OlOzY0rYfmDVByElSfDYF9f7vNHzsyp5SEEMepWFM+mIw35/P0HoelaHtvoOm9PjKbzfKjH/0IAI/Hw/3333/AYLK3qqqqfdYf/vSnPyWZzC+X+u53v3vQYHIPVVW55pprDunYY5UElOKEkq8frqCFyrBjGULeMKFEE0mlm0aKabP8OL0jSwewTeKZDFucIG53CZaq4/b4QenEUN+zhtJxoGVz/lE8CarPglDl3tdH8JSSEOI45Dj5n0nJjnww6S3MB5NKPzuOFRVGaCnanTt39jwfM2ZMz/Ply5fT0NAAwBVXXEFlZeU+5x4qx3H4/e9/D+RHJ2+88cYjbmskkoBSnFB66of7/bRnK7D1LGpAxZfuYkKultGKQROFNJs+LAewcoCTT7BuBEgrOo1WmKDjJRIYhTKqEzrW5CvvvFfblvwjMi4/Ymmbx+2UkhBihOramf+507l978hkf8Fkb4qaP65xZJSi3bx5M8uWLQNg3rx5lJWV9bz2wgsv9Dw/2g0xGzZsoK2tDYBzzz2XYDB4VO2NNBJQihNK3/rhHjrCJ1Oc3J5ft2iZuLIxxuS6GaV105Lz0aQGySg+UFVQDVJGAXbawWNoJHMKDzWP5eSSk5itv0uk/S3I9ZOXsmN7fo1lVy24/Pnpot0/uDOeYuKWjmkrGKpDoDCHO902IqeUhBAj0K5V+dmSdPehBZN7KCqEq/IjlcdAKdqWlhbWr1/f83fHcejq6urZ5Z1KpQiHw/z0pz/tc96aNWt6ns+ZM+eo+jCQbY1EElCKE06f+uEJiFSeipqNQrQRki7AQQMqgHJHoV3302iFiVkGsVgGt6701A/P2Q7rmtOso5LxkbGc5qpjVPebYPZOku7kp7BTXRBvxvFGqA/OZk2snG2NbuxeU+wqDhMCBcwMV1AVX4sygqaUhBAjTC6Tn0WJNeZnS3xFh3e+ryh/3jFQiva+++7jvvvu6/c1VVX5/Oc/zy233MLkyZP7vNbe3t7zvL9d3YdjINsaiSSgFCecfeuHJ5hUGkYtLQA7l99xbVugaiiai2JVp9CyWburG11VCHoMfC6NkLfvt8+7HVnepYzK4KWcFmxgfOwtFDOR/83fTEK6kxYKWZ48g7Z3FVJqnGY8JPBgOQqa4uDXbBKWypa4h2LjbBbwGqUjYEpJCDECZWL5jTaZ+O6lN4dZqUVR8+dl4rvbOTZL0dq2zSOPPILH4+H73/8+bvfePsZisZ7nfr//qK4zkG2NRCO7zo8QR2BP/fCI38XksiDdqSwbGqK0xTPYipafjnYHwfBhKxpt8QwbG2OYls3ZE4s5c0IRHzqlAm0/ZbIaYiZPtJTwoHIxGwrmYyU7wTLZmfazOHsmO7IFbEwXsyZeQHsigzvbSVBJ4VZt2k2dNd0+NkY97Eh5WJyazc6os3dKSQghBsqe0rGOlV/HfSQUPX9+7/aGwe23347jOH0eyWSStWvX8vWvf514PM4999zDBz7wgZ5d2ECfdY6JROKo+jCQbY1EMkIpTkhHWz+8ushPPJNjTV0Xa+q7yJj2PtdoT1r8Ix7mte7TGZvxsjGj0q6FqckW4VVNJrk6KNRSqApgA4ob2xemwwnQmHaxMeplcgCWJadwRXsrpa7hnVISQhxntN3JvRUtvynwSDi5/Pm92ztGeL1eTjnlFO6++24mTZrEZz/7WV5++WW+973v9VTLKSraO83f3NxMKBQ64uu9t60TjYxQihNWdZGfK+ZUMbbIx9TKMDOrwhQFXGRyFrG0SSZnURRwMbMqzNTKMGOLfFwxp4rqovxURsCtc/bEYj59zjjOP6mEoKef38+sLLGcxkOdU3nRmclKcyx+Lcc0dytF+u5gsufYDGqyheLsLqZ5OwgbOWriXjqUEMtbi3Ds3VNKQggxENzB/LS1O5Bf4+3s+4vxATl2/jx3YHc7x+6u5k9/+tNEIhEA7r///p6vz5w5s+f5qlVHNws0kG2NRBJQihPanvrhl8+pYsboAsYXB5hWGWZGVQHTKsOMLw4wc3Qhl8+p4pp51ZSGPPu04dY1Zo8p5Iazx7FgWjnFgV6/pdsW0ZxK0tbpyHlIOh7iehE7lSrS7GekcXdgOcnZiZc0tZkgbTk39WljWKeUhBDHGd2dz5cbrMj/bEm2H/yc3pLt+fOCFVAy+ZiePVFVlUmTJgHQ2NjYs4Gmd0nEJ5988qiuMW3aNIqL85XSXnrpJaLR6FG1N9JIQClOeHvqhy+cUcnn54/nujOr+fgZY7juzGo+P388F8+oYHTEh3KQHdaaqjC1MsQ186q55NRKRhV4QdVoyhiYjkrGVgnqFo6i0GyHWGOPZ4szmgT7BqkAqp2hwm4kms6RMmFtl/eYm1ISQoxwo2bn1417wvl0ZYc6Smlb+eM94fz5lbMHt58DIJfL7fN8wYIFPcnMFy9ezK5du464fUVR+OQnPwnk11D+9re/PYrejjwSUArRi1vXKAq4KQ97KAq4ceuHX0tcURTGlwS48vTRfGRONaCQcNyojoVX2/vD2kGh3QmyzhrPZruaqOPDeU/hnYiawrAzNGc0tjZ3kelqYp+DhBDiSBVU56tyFY7Lb/5r3XzwoNKxoe2d/PGF4/LnF4w58DnDLJlMsnHjRiC/tnLPSKLL5eJrX/saAOl0mk9/+tNYlnVIbe7atYt//etffb52yy234PP5ALjtttvYvHnzIbVl2zYPPfTQIR17rJKAUohBFPT7mFzqp8xrU6rHUe1+pqwV6MLPRnssG+yxdNiBnphRdUzCaoIEPmxFJ77+7/DWA/mcbxJYCiGOlqLkS7z6IvlCCqnOfAWcROu+gaVj57/euCZ/XOnJ+fNOXnTM58i94447SKVSQH5UUtP2DhbcfPPNXHDBBUC+FONHPvIRWltb99uW4zg8/PDDzJkzh7Vr1/Z5bdSoUfziF78A8qOU559/fp9qPP3ZuHEjF110ET/84Q+P6N6OFbLLW4hBZFoOhCrQtCSjPBkq9Taa9Cqaswa2854fwArE8VHjjMHjpKikjWKzCU0NYCkauIOYdgJizbD+cQiUwthz82ugDueHeS6T39xjZfNT6O7gMb32SQgxyILl+RKv65fkCyl0bs+PVL63NGyqK/9zwxOG8hn5YHL6ZcdEFa/3VsqB/Ijjli1b+MMf/sDTTz8NgMfj4bvf/W6f41RV5dFHH2XhwoW88cYbLF26lAkTJnD11Vfzvve9j6qqKgzDoKmpiddff53HH3/8gCOPN9xwA/X19dx22220tLQwf/58LrzwQi655BJOPvlkCgoK6OjooKamhieffJKnn34ay7L6bOoZiRTHkWEOcXTq6+sZPXo0AHV1dVRVVQ1zj44d7fEMf3htBxs2rMNtxZlo7wDDS85TTFPWRVPaIPfewBLAAcwELitJTtHxag6nlLm4bkw7Ra73TMcEy/OBZdGE/QeWjpOv2btrVb4yRu+RB0XNB6WjZuenv47xkQYhxCCJNeWrciXa8tPZscbdScutfGogdyC/Acfw5ae5T140rMHk888/3zOyeChKSkr44x//yIUXXtjv6+l0mv/4j//gvvvuI5s98AZIRVG4+uqr+cEPftCzBvO9lixZwq233sqOHTsO2rdp06bxk5/8ZL99G0iD9ZktI5RCDKKAR0dVVPxFlbQ31WH7SlCTreh2E1XuEBVhH61Zg8a0i4yt5ANJ28yPIjoWGVchjaaPgMtNoRlDVdr2vUisCdYthlBFPrCMjO8bFB7Kh0Q2ni8PeQx8SAghhkmwPF/itasWdr0FbYF9f/ksmZzfgFMw5pj/5dPlchGJRJg2bRof+tCHuOGGGygsLNzv8R6Ph3vuuYevfvWr/OlPf+Kf//wnNTU1tLa24jgOkUiE6dOnc/7553P11VdTXX3gymWXXXYZCxcu5LHHHuOpp55i5cqVtLS0EIvFCIVCjB07lnnz5nH55Zczf/78g278PNbJCKU4ajJCeWDL1jawtq6LNdubmKjsoliNQ7oLcul8QGd4cByV9pxBQ8ogaeug6qB7SDouOpwgZYUBykNupoYyTGcrc7xNBPX9LJwPVcK4c/OL5Tu356exkh355+nuA09jFY7bO40VGT+E75IQ4pgjy2OOSzJCKcQINbOqgC3NcULBII3mWCLaLlTdDZYJ2Rjksig4FKtZikIuutUCGrIeumwPMTuIW9MxNJWykBfTG2K1U8zaZCsnO9s4zdtM4XunwKMNsObP+R/+scb8h0Lr5vw0VckUMp5i4paOaSsYqkOgMIc73ZZPAdK0Nr/Qfv0SmHWNjFQKcSLT3RJAikMmAaUQg6yq0EtxwMWYIj8bG3JscU9kUpGFGmuCpIv8PHeegkKBv4hQoIx1bdDVlSLgMfC5NELe3d+uioLlK2W9U8KGZCuT49s43d9Cibt36TQH3n0+P/qY7sIJV1MfPp01MT/bGt3Y7J1aUXGYEChgZriCqvhalJZN+YX5m5bmp79G+DSMEEKIwScBpRCDTFEUFkwrZ/Fb9UwuC1LTHGNDTqeiYDyRoomotplPEqxq2KpBR8qmsT1NJpfjrAnFBDw61RE/jd1p7N4rVBQFx1/KO04J7yRaGB97l9P9rVR6zfzUtpmEdCctOT/L28bT1p4jZWg059wkLBXLUdAUB79mk7BUtsQ9FBtns4DXKO3cnh/h7KqFwgOvExJCCCEkoBRiCJSGPCycUcGytY3omkpte4KtLXEMTSXsNdBUFcu26U7FMS2bkEdnamWYiN/FwhkVVBf56U6ZrNrZyfpd3eTsvoElgTLedUp4N95CVWw7Z2RqGZMzqU37WWafSYcTotYMELUVDC2Vv6auYzkK7aZOU8YgpFuM8aksZjYLM69RXZSEhlUSUAohhDgoCSiFGCLVRX6umFPF8g1NBNw6qWyO5liGRCaHZTtoqkJRwEVZ0I3XpVMccLFgWnlP/fCw1+CCKaWcMS7C6tou1tR3kc29ZwdmsJx6q4j6bd344n6aMwY5PcgWswivajLJ1UGhlkK1AdULnjC25qYjq9GYdrEx6mVyAJYlp3BFeyulrpr8GkxZRyWEEOIAJKAUYgiVhjxcM6+a+s4Ua+q72NaS6DONrSoKE0sDzKgKU1Xo7TeNhN+tc86kYk4bW8iaui5W13WRyvbamGObYAR43ZxEl23QlXJTqUeZ6mpF610bK5eCeApV91LsCRMJWWyJu6mJe9GNEMtbba4ps1EyMQkohRBCHJAElEIMMUVRGB3xMTriI5OziKdzmJaDoSkEPPoh1w/3GBpzxxcxa0wh63Z1s2pnJ/FMDmyLaE4laet0Oz4c3cDUbdZmK6jUo5RoCdTeceqewNLwMckTZoMdpjYTJECK+rTBaOvACX6FEEIICSiFGEZuXcMdOLQAcn9cusqc6kJmVoXZ1BjjzS1ZajIGpuOQsVUiLhtFd5FxDLZbPurNFBV6lFI9ga70WotpJlHNJBWk2WoGSRkGa7t9jNZcR3mXQgghjncSUApxnNA1lVOqwkyMGLy7cRW7FAPVsfDumedWFNDdmJqLWstLQzpFuR6jXI/3CSwjdieGCc1pja1RjYzmRya8hRBCHIh68EOEECNJ0taIFIQp8TiMd3UQVFJ9D9gdWOZcYeqdUlalK6nNhjCd/I8D1TEJqwkStoGd6ia+8Zl8pR0hhBBiP4ZthLK1tZV3332XpqYmEokEhmFQUFDAmDFjmDhxIpp2dNOAQpyoTMuBUAV2Y5Kwy2GsvouoZxQNGRfdZq/vq92Bpa25aLA8NKWDlKlxKpwmNFXB2l3n22x+B6KboPwUGHt2vkSjEEII0cuQBZSJRIK//e1vPPXUU7zwwgvs2rVrv8e63W5mzZrFhRdeyEc+8hFmzJgxVN0UYsQzNAU8YTTdwFICKHYbYbOZcKCYuKWxK2XQafb61u8VWDZmoNmqIqe48WsO6AaGGgfHhsY10LweKk6F6jPzic8PhdQDFkKI496gB5SrV6/m5z//OYsXLyaZTALg9K720Y90Os1rr73G66+/zne/+12mTZvGl770Ja699lp8Pt9gd1mIES3g0VEVFX9RJe1Nddi+EtRkK8SbCLhDnBTwkbRUdqVdtGf1fOVH28wHfoDljtCUdeF3FEKJDFbvb1fbgl1v5YPLUbNhzJng6ud70nGgayfsWgVtW/IB6R6KCsWT8ucXVEtpRyGEOA4ozsGiuyO0evVqvv3tb/PUU08Be4PI8vJyzjjjDObMmUNpaSmRSITCwkJSqRQdHR10dnZSU1PDypUrWbt2LaZp5juqKBQVFfGNb3yDm266CbdbRjiOFfX19YwePRqAuro6qqqqhrlHYtnaBtbWdbFmexMTlV0Uq3FId0EuDYoGhgdQSdkKDUmdtpwHRzVA95B0XHQ4QcoKfJTrcU7SmzjJn+SMwgQRl9X3QpoBVafD6Lm72wRiTfk64Im2fPnHWCNk4uBY+Wu7AxCsAMMH/mI4eREEy4f6LRJCiBPSYH1mD0pAecMNN/Dggw9i2/lRidmzZ3P11Vfz0Y9+lDFjxhxyO9lslhdffJGHHnqIv/zlL0SjURRFobq6mj/84Q+cc845A911cQQkoDz21HUkeeytejY2dGOZaaZpu1BzSbBMyMYglyU/NKmA7iKjhdhl+mgxfTTbQVRNpyToYWpFiJDLge46lFgTk/0pziiMU+x+T2Cpu/NBpa8INi/Lb+Lp3A7pbjKKh7irGBMXBlkC2TbcTjq/FrNwHPgiMP0yiIwfjrdKCCFOKCMqoFRVFZfLxSc/+UluvfVWJk+efNRtZjIZFi9ezPe+9z02b97MHXfcwW233TYAvRVHSwLKY4/jOPzx9Z3saE+ysaGbsNdgUthCjTXt3rHd+9teAX8RdqCMTR0K9V0pfC6dsNdgRlU4/zrkRze76yDWwkR/irmRBKXu3N5msnFo2QQuP06inXqlnDVMZluuGLtXQgkVmwl6GzOpoYoWlLKTIVAGs66RkUohhBhkIyqg/Pd//3e++c1v9nR4IDmOw+LFi7Esi49//OMD3r44fBJQHptaomkWv1VPSzRNTXMMr6FTUeAh4lVRbTO/HlLVsFWDjpRNY1ealJljclmQAp+LCSV+ajuS+V3jvZmpfGAZb2GcL8PcSIIKTza/XjLVRUt3kuXWbNr8E0lpIZozLhKWiuUoaIqDX7Mp85h4VZvibD0LPBsorZoIkXFw+mdkTaUQQgyiERVQihOLBJTHrp3tCZatbaQjkaW2PUE0ncPQVMJeI58ayHboTpmYlk3IozOmyE/E72LhjAqqi/yksharajt5u66LbM7u27iZhK5aSLQxRu9krrkCK9HOsuR0OrRias0wUceLYRiEPRqaApaj0J3TMG2FkG4xxpshkqlnYWEt1dXj4dRPQGH18LxZQghxAhisz2yplCPEcay6yM8Vc6pYvqGJgFsnlc3RHMuQyOSwbAdNVSgKuCgLuvG6dIoDLhZMK6c0lN9g43VpnD2xmDnVhayu7WJ1XScZc3dgafigZAqE49TufI1N3dOoT7vw6tBoFuLTTCYZbRRqKVTbyK+ZNHzYKHRkNRrTLjbGfEz2lLCsLcsVRSalDaskoBRCiBFIAkohjnOlIQ/XzKumvjPFmvoutrUksHtNTKiKwsTSADOqwlQVelH6mXL2GBpnTihidnUBa+u7eWtnJ6ns7o05ugcMH9tsgy7boTUTJKxlmKh3ENHS+Rls24RkG6guVE+YYpeXiMtiS9xNTboAXelkeb3NNYEalJMykqdSCCFGGAkohTgBKIrC6IiP0REfmZxFPJ3DtBwMTSHg0XHrh1aZyq1rnD42wsyqAtbt6uatnR0k4kmipkLS0uhWfeiKjl9NsyVbhF/NMsqIUajuCSyzkGwFzYXqKWCS32FDzEetWUQg1UZ9SmN0JiYBpRDioJ5//nkuuOACAG6//XbuuOOOg55z/fXX8/vf/x6A7du3M3bs2P0em0gkeOSRR1i+fDmrV6+mtbWVZDJJQUEBEyZM4Mwzz+TKK69k3rx5/Z7f3y/nAIZhUFhYyPTp0/ngBz/Ipz/9aQoLCw/a92PdsASUuVyOJ598kpdeeol3332XWCyGZVkHPEdRFJ599tkh6qEQxy+3ruEOHF1pU5euMqe6kBlVYTZs2cbvt2qYjk3G1oh4QNH8YOdI5DRqMq59A0srC4kWVN1DhVHM1rSLlK2yttvHaCs7MDcqhDgqR/PL50j361//mttuu42WlpZ9XmttbaW1tZXXX3+dn/70p5x++uncc889nHXWWYfUtmmatLS08K9//Yt//etf/PjHP2bx4sUjPhXikAeUL7zwAtdffz21tbU9XzvQviBFUXAcZ7+RvhBi+BiayskVYcZ4snRoOTptC6+m5jMNaTqoAbDNnsDSp2ap6h1Y5tJEnHoMq4zmrJetcTcZR0fGJ4UYHo7jHHB5zIRSPzOrCva7PGaks22bL3zhC/zmN78B8mkQL7nkEj70oQ8xceJEQqEQra2trFu3jr/97W+8/PLLrFy5krvvvpu//vWv/bZ52mmn8cADD/T8PZvNUlNTw7333stLL71EU1MTixYtYv369YwaNWoobnNQDGlA+fbbb3PRRReRzWZxHAePx8OkSZMoKChAVdWDNyCEOObE8YKiYmgaJ9NKgT/MroyLtLUnsDRANcA2SebUvYGlHqNQS6MCYbubRNaNnWgnnkzjDg/3XQlx4mmJplm+oYm2eLbfDXx+t04iY7KlOb7PBr7jxXe+852eYHLSpEk89thjzJgxY5/jFixYwNe+9jVeeeUVvvzlLx+wTb/fz/Tp0/t8bfbs2Vx55ZVcddVVLF68mK6uLn7yk5/w4x//eOBuZogNaUB5xx13kMlkcLvd/OQnP+GGG27A4zm+/jMKcaIxMcAXwdJi6HaUEjVKcchPe1ZnV9ogtU9gmc0HllkXPtWkSu1AdSwszQBMzDWLIToVqs8GT2i4b0+IE8KhpBhrj2dp6k73pBhb/FZ9T4qx48Fbb73FXXfdBUBlZSUvv/wypaWlBzzn7LPP5tVXX+Vvf/vbYV9PVVW+//3vs3jxYgCefvppCSgP1csvv4yiKHzrW9/iC1/4wlBeWggxSAxNgVAFWlMCy3JDpgvF8FHszlHkyvUTWLpAdYGVJZlTqMkWknBcBBwHx6VhKHFoeBua1sOoWTDmLHD5Dr1DuQxkYvl1mpoL3EHZ5CPEAbRE0yxb29inCMKk0gCFfhdqr2lt23HoSGRp7EqzsaGbyWVBlq1t5Io5VcfFSOV///d/95SM/vnPf37QYHIPt9vNlVdeeUTXHD9+PEVFRbS3t7Nz584jauNYMaQBZTqdBuCiiy4ayssKIQZRwKOjegvwuw3azTB2rhU12Qa+YhRFOXBgaZs4ika3E8C0LdYnNZrSGSKGhWLnoG4lNK6BqjNg9Bn7DwwdB7p25qv1tG0Bp1cSdkWF4kkwajYUVEslHiF6cRyH5Rua6EhkqWmOEfa6mFQaQFX3/T5RFYXigJuIz8WWljg1zTF0TWX5hiaumVc9otdUdnV19ayBHDt2LJdeeumQXdswDICDbk4+1g1pQDl27Fg2bdqEaZpDeVkhxCBy6xoTSgMkYmNpqtlOh6uSYrMB4k3gDoHhe09gqbErDqlsDhyLlFGAbXnwB3Q8eox/tIRZ1eVnbiTOJH8GJZeFHS/Drreg+iyonJ3f8LNHrAk2LYVEW756T6wRMnFwLFA0cAfydcZb3wF/MZy8SGqGC7FbfWeKtnh+mnvPyGR/wWRvqqowqTTAhoYote0JAm6d+s4UoyOHMZNwjHn55Zd7ArqLLrpoyPZ1tLa20tzcDOSn2UeyIQ0oL730UjZt2sSLL77ImWeeOZSXFkIMoplVBWxpjhMqKqOx2ybit1AzXflk5ooGhgdQUbApNtMUqRbt3jB1dgnNGTdurx8jGKSstAoyjbTFmnmyqYBiV25vYGmmYOuzULcCxp4D5TOgawesXwLJDujcDuluMoqHuKsYU3FhkCUQa8MdbchX6ikcB6v/CNMvg8j4YX7XhBh+a+q7SGVzRNM5Jh5CMLmHqipUFHjY2hInlc2xtr57WAPKlpYW1q9ff9Djurq6+v36mjVrep7PmTNnoLp1UHfffXdPppv58+cP2XUHw5AGlDfffDO/+93v+NGPfsTHPvaxAyYUFUKMHFWFXooDLsaUl7DRhC2mziS/G9U2IRuDXBZwAAVcPhRXkIhq0JbyEvYU4PP68BgqoYAPAhMhPAo6a2lLtPYElmcUxpkUyKBmYvDOU7DlGUi0gmPjtGymnlLWMJNtZjG2uXd0QcVmgt7GzHQNVY1rUcpOzgehs66RkUpxQsvkLLa1JGiOZTA0lYjfdVjnR/wuDE2lOZZha0ucTM4atjyV9913H/fdd98Rn9/e3t7z/FDXTh6pPWmDfvGLX/DrX/8aAF3XueWWWwb1uoNtSAPKkpIS/v73v7Nw4ULmzp3LXXfdxZVXXkk4LDlChBjJFEVhwbRyFr9Vz+TKImqaXWxQSqgwuoioLlRlby4721Ho0ItotMKkvC5OLQtSEnQzp7qQrS1x2uJZ0L1QchKEq6BrJ23JDv7eXEBRZ465hXEmBdKodW9AqouWuMly9WzavONJ2RrNaYOEpWI5Cpri4NdsEp4ytqglFGfrWdCwgdIqIz9NfvpnZE2lOGHF0zlsxyGRyRH2Gn024BwKVVEIew0SmXw78XTuqIsmDJdYLNbz3O8f2F3rL7zwwgHXlxqGwf/+7//uk1popBnyxOYzZszgxRdfZO7cuXz+85/nC1/4AsXFxfh8Bx4qVxSFbdu2DVEvhRCHqzTkYeGMCpatbUTXVGrbE2xNuzDUUsJu0HCwUOjOOJimQsijM7XIT8Tv6kk9Mm98EVtb4rz+bns+sHT5oXQqZKLQuZP2dHc+sGyJMjfnwZWEv6dm0aH5qI2bRFUvhq4R1i00xcZyFNpNnaaMQUi3GOMdw+KYysKmWqrdQeiqhcLq4X7rhBgWppX/RW9PnskjsSelUO/2hsORlF7sLRgM9jxPJBID2bX9Ki4u5qKLLuLrX/96v7kuR5ohDygff/xxPv3pTxOLxXAcB8dx+i1t9F4jefeYECeK6iI/V8ypYvmGJgJuvd/kyEVBnbKgG69L3yc5sqIoTCoLMrE0kA8st3fQFsvkN/eUT4dUF3TtpL2jlcWpyWxPzcSvO7SYYXyaySS1jkINVE8B6PnpO9uBjqxGY9rFxpiPyZ4SlrVluaLIpLRhlQSU4oRlaPnP1d5B4eHqHYzuaW8kKioq6nm+Z5PMQHlvpZw9tbwHe2p9qA1pQPnaa69x1VVX9eykqq6uZsaMGVIpR4jjSGnIwzXzqg9Yvm1iaYAZVeH9lm/rHVhua43z2ru7A0tvYT6vZKyVbd1eumyDd7JhQlqGCXoHES2NYgHxFBh+8IRRNYNit0XElWJL3E1NugBd6WR5vc01gRqUkzKSp1KckAIeHVXJV8Bpj2exHeewpr1tx6E7ZVIUyOerDHiGfIxqwMycObPn+apVqwa07f4q5RyPhvRf/6677sKyLMLhMA899BAf+tCHhvLyQoghoigKoyM+Rkd8ZHIW8XQO03IwtPyHzqEu3FcUhYmlQSaU9AosO5JEFT9JxaJb8aADAcVka7aIxt21wgvUNIqZyKcRcu0OLFWdSYEMG6Jeas0iAqk26lMaozMxCSjFCSmf8stPImPS1J2mI5GlOHDo3wsdiSymZVMWdDOxNDBsG3IGwjnnnIOmaViWxVNPPYVt2zLQdZiG9N168803URSF73znOxJMCnGCcOsaRQE35WEPRQH3EX3o7Aksr5k7hkVTQiQtFdNRyGAQ9BgohhdQSNgu3skUsT5TQqflzqfjyMYh2gCpTlTHosKTJWq5SNkqa7t9+Yo6QpygZlYV4HXphDw6jV1p7EOc+rZsh8aufBlGr0tnRtXI3lxbUFDQk8x8586dPUnOxaEb0oAymUwC+d8EhBDicCmKwuiiABVuk7CWxaPm8Gp2fr2kOwi6h72BZTEbMsV07wksM1GINRCxOzAUi2bTz9a4m4wzcqfphDhaPSm/ivykzBxbWuIHDSpt28nnnzRzjCnyUxxwUVXoHaIeD57//M//7BmVvOmmmw5pfwdAJpPh0UcfHcyujQhDGlCOGzcO2BtYCiHE4YrjxVFUNE3nJKONSYF0PqhUlPzUtTsImhtQiNtuNmWK2ZgpJmq5wLFR012EzRYStoaNQpyR/0EoxJHak/Ir4ncxuSxIdyrLhoYobfFMn7XPkF8z2RbPsKEhSncqy+SyIBF/fmPd8bBxds6cOfy///f/AGhoaOCcc85h3bp1Bzzntdde4+yzz+bhhx8eii4e04b0V/PLLruM9evXs3z5chmlFEIcERMDfBEsLYZuRylWohSF/LRldXalDNK2mq/Mo7sglwErS8x2szFTQkjNUKW1ozlWfnNgJorZXguhqZKPUpyw+k351RLH0FTCXqNnF3h3ysS07HzKr8pwT8qvPVkajge33347TU1N/OY3v2HLli2ceuqpXHrppVx88cVMnDiRYDBIa2sr69ev54knnuCFF14AoKqqaph7PvyGNKC89dZb+dOf/sQ999zDJZdcwmmnnTaUlx8WTz/9NL/73e9YsWIFTU1N2LZNSUkJs2fP5hOf+ARXXHHFUS38NU2TZ599luXLl/PGG29QU1NDd3c3fr+f8ePH8/73v58vfOELjB8vZebE8cHQFAhVoDUlsCw3ZLpQDB8l7hzFrhxtWZ36lEHGVsHw5kcrc2mwTaKWm43ZYtLoBFUHNA1jy5PQ+SaMny8phMQJ65BSfgVc+035dbxQVZVf//rXzJo1i9tuu43W1laWLFnCkiVL9nvOWWedxbe+9a0h7OWxaUgDymAwyLPPPssVV1zBeeedxy233MLHPvYxJk+ejMdzfP2nzGQyXH311Tz++OP7vFZfX099fT1PPPEEv/zlL3niiScoKCg47Gu0trZy8skn9ykZtUd3dzerV69m9erV/OxnP+Puu+/m5ptvPpJbEeKYEvDoqN4C/G6DdjOMnWtFTbaBrxhFUShx5yhy5WjN6uxKuciigssHdg7SURzHoYMgadumJqWSyCUpijbA2w9D0QQYdz4Ey4b7NoUYcgOR8ut48fnPf55rrrmGRx55hOXLl7N69WpaW1tJpVIUFBQwYcIEzjrrLK666ipOP/304e7uMUFxHGfIUttr2t7dnY7jHNZ/RkVRyOVyg9GtQfG5z32O3/zmN0C+Lug3vvENZs+ejWEYrFu3jh/84Afs3LkTgAULFvD0008f9jXq6+sZPXo0AKeeeiqXXHIJc+fOpaysjO7ubp566il+/vOfk06nAfj1r3/NZz/72QG6w/77UVdXJ0P/YtAtW9vA2m27WFOznYnuLorNBtCMfAJ0w9czfW070JLWaEgoZE0THIuk4qPDNCjz2pR7bSYFMkzwp5kXSVDqzuXPLZ0K487N5708FLkMZGL5HePang1CkopIjGxHk/JLHLsG6zN7SAPKo5naVRSlJyH6sa65uZnKykps26awsJC1a9fu8w8WjUaZOXMmO3bsAGDlypWHvQRg165d3HDDDXz3u99l3rx5/R7zxhtvcMEFF5BKpQiHw9TV1fUpMTUQJKAUQ62uI8ljb9WzcWcjVncj07ydqJmu/NS2ouXXUKICNphpbNuihULq7GIasj5Ud4ASn8pUdxshZe8mwUmBNPMK4xS7LVA1qJwF1Wflc1m+l+NA107YtYpMy1biJpi2gqE6BAxwl06EUbOhoFrWZwohjhmD9Zk9pFPet99++1Bebti88cYb2LYNwA033NDvP1YoFOKWW27pmYZ+7bXXDjugHDVqFP/4xz8OeMzcuXP54he/yI9//GO6u7t55plnuOyyyw7rOkIca3pSnZSXsNGELabOJL8b1TYhG4NcFnAABVw+VFeQUtWgM+Uh4CnA6/Hgc+uERlVBvAm66sAy2RL3sDXuZnIgw7xInEj9m9C4BkbPhdFn7B11jDXhbFxKfVs3azo0tnUq2Dmz55qqbjChcCczd75LVXEYZeoiCJYP3xsmhBCDTALKQZDN7k2UfKDNMBMmTOj3nIF2wQUX8OMf/xiAbdu2Ddp1hBgqe1KdLH6rnsmVRdQ0u9iglFBhdBFRXajK3okX21Ho0ItotMKkvC7mlAUpCriZUh5kW2uCdLAS/GUQ3QXRXTi2xTtxDzVxN1OCaeYVJijY8TI0rILqc8ATomXVkyyvU2mLmqSyKZqtIAkljOVoaIqFP50kkYqxpU2nuCnFgu5HKD3twxCRzXFCiOOTZPQdBCeddFLP83fffXe/x/UO7nqfM9AymUzP897rWIUYyfpNdZJ2YailhN2g4WCh0J1xME0ln+qkyN+T6qS6yE8mZ/F2bRdv1XaSKRgDwQroroNYI44Dm2Je3ol5mBpKM7cwTmjDEnbuamRZYgodCZNaK0JUCWLoGmHDRlMcLEeh3QzSlCsmlIwxJtvJYjPIQmsp1ed8TEYqhRDHpSFdQ3kiOfvss3n11VeJRCKsW7eOysrKPq/HYjFmzpzJ9u3bGT9+PJs2bcLlcg1KX26++WZ+9rOfAfD3v/+dD37wgwPavqyhFMOpJZpm+YYm2uLZflOd+N36QVOdpE2L1bVdrKrtJJuz82sxu2ohvrdShqbYjEnVsD1p0JV2qLFH4fUFqPRDoctC7bVM0nagI6vRmDZIpdNM1lsoLQxxxRQ3ped9StZUCiGGzXGxKac/zc3NrF+/no6ODgAikQjTp0+nrGxkp+2oqanhoosuYvv27ZSVlfXs8tZ1nfXr13P33Xezfft2iouLWbp06X431RytxsZGTjrpJGKxGCUlJdTW1h52iqb6+vqDXuOMM84AJKAUw8NxnAFJdZI2LVbt7GR1XVc+sMwmoHMnpDrATLO2zSFqKiQdF1WuJFM87aiGBzwF/e7qth3YEnfTnUgx1dvJ2JIw1yy6ECUydhDeBSGEOLjjYlPOHo7j8Jvf/IZf/OIXbNy4sd9jpk6dyk033cSNN944InNdTZ48mZUrV3Lffffxgx/8gFtvvbXP64Zh8LWvfY2bb7550AIwx3H43Oc+RywWA+Db3/72EeX73PMfT4hjlaIojI74GB3xHVWqE4+hcdbEYmaNKeTNnR2sqVMwXVMh3U303ZUkbZ1Oy4Wt6sSdHA25IOXE0XNNYPjBE86nL9pNVWBSIMMGU6c25SGQcqjfsprRc8cO0jshhBDDY0hreQN0dnZy3nnn8cUvfpGNGzfiOE6/j40bN/KFL3yB8847j66urqHu5oBYunQpDz30EPF4fJ/XTNPk0Ucf5eGHH2awBom/973vsXTpUiC/MedLX/rSoFxHiGOJW9coCrgpD3soCriPKG+e16Vx7qQSrj97HKeOLkDzBGiyCzAVFxkMgqqJjUq9GeLtdBkNZgArm4RYIyQ7wN6b4kxVoMKvELU9pFJJ1m5vyuetFEKI48iQjlA6jsMll1zCK6+8AkBRURFXXnklc+fOpbw8v1C9qamJFStW8Oijj9LW1sarr77KJZdc0lMvcyANxMjnAw88wPXXX7/P12+99VZ+8pOfAHDppZfy9a9/nZkzZ6JpGps2beLnP/85DzzwAN/85jd54403ePTRRwd0w8xDDz3Et7/9bQDGjRvHww8/fMR5QOvq6g74eu8pbyGOJwG3zgVTSplemOPODTYJW0fVDbxuB3I2YJNzNGrNME25AJVGjFInhmom8knW3UFQVCIuC0NXaU7rbI25yCS6cYdLh/v2hBBiwAxpQPnwww/z8ssvoygKn/jEJ7j33nv7TbJ93XXX8f3vf58vfelLPPjgg7z88sv86U9/4uMf//hQdveIPfnkkz3B5PXXX88DDzzQ5/VZs2Zx//33U1VVxZ133smSJUu49957uemmmwbs+jfccAOO41BeXs4zzzzTE7AfCVkTKU50qm0yzp+lK24SdxwU3Z2f2rayu0cbHbKOxo5sAY1KgCojRrHdhZKJgacA1eUnrOdI5HRsFOLJFO7wcN+VEEIMnCEPKAHOP/98HnzwwQMeGwgE+P3vf09tbS0vvPACf/zjHwc8oNy0adNRt1FRUbHP1377298C+RHQu+66a7/n/td//Rc//elPicfj3H///QMSUD7//PNcfvnlmKZJYWEhy5cv75PvUghx+Ezy6yI1BaqMOKUhm/qUi07TnS+1mMuAlZ/Gzjg627KFNOwOLCN2O0omimaFsBx3n/aEEOJ4MaQB5apVq1AUhX//938/5HNuuukmXnjhBVavXj3g/ZkyZcqAtwl7A9XS0lJGjRq13+M8Hg/Tpk3jjTfeYPPmzUd93RUrVrBo0SLS6TSBQICnnnqKGTNmHHW7QpzoDF8QUNBUDSuXwa9ZnBRME8up1KdcdCse0F35dEOWCUDKMdiSjeBTs4zWY+TSCTTdgZyzuz0hhDh+DOmmnD2pgcaNG3fI5+w5ds+5I4Gu5+P0XC530GNN0+xzzpFau3YtF110EfF4HI/Hw9KlS5k7d+5RtSmEyAv4faj+CH6PQbdlYGfz9b+Dus3JwTRTgykChgOGD1wBUPd+PydtF5vTBdRYpYCCmu0isPOf+c07QghxnBjSgDIczi8aamhoOORzGhsbgXzt65FiTxDc3t5+wGn1jo4O1q9f3+ecI1FTU8OFF15IZ2cnhmHw+OOPM3/+/CNuTwjRl1vXmDB2LGVeG1Nx05HMQq/sDCHDZlowzUmBNH5DAZc/n0ZI0cCBlKWSctx02D7alAgdu7bBiv+Fmn/kc10KIcQIN6QB5fTp0wH22aRyIHuO3XPuSLBo0aKe51/5ylf6rdNt2zZf/vKXe15buHDhPsfs2LEDRVFQFGW/AWJtbS0f+MAHaG5uRtM0Hn74YT70oQ8NzI0IIXrMPGkiXo+XkEenMePBTrT1CSoVJV8xZ3ooxaRAGq9LBZcfW1GIOR7cqoOhgq67eKQ+wt92hWh5dw288SvY8Qrk9v05sV+5DCTaINqQ/1PSEAkhhtmQrqG8/PLLef755/nLX/7CHXfcwe23337A1D133nknjz/+OIqicMUVVwxhT4/O9ddfzz333MOmTZv4xz/+wWmnncZNN93UkzZo48aN3Hfffbz22msAlJWV8dWvfvWwr9Pe3s4HPvCBnrQ+t956K1OmTOkZ9exPYWHhAdd1CiH6VxXxUVw9lTHpt9mYC7MlYTHJbkL1hPJT3bt/likKFBk5Ik6UlkSWt61icrqfEiODz+0jpKcBeDfp5t2km8mBNGcmXyHSsArGngvlM6C/FF+OA107YdcqMi1biZtg2gqG6hAwwF06EUbNhoJqKe0ohBhyQ1p60TRNZsyYwTvvvIOiKEybNo3rr7+euXPnUlpaiqIoNDc388Ybb/D73/+e9evX4zgOJ598MmvWrDnqdYZDaefOnVxyySWsWbPmgMeNGzeOJUuWcOqpp+7z2o4dO3qmws8//3yef/75Pq8///zzXHDBBYfVr09+8pP87ne/O6xzDkZqeYsTRUs0zeKX1tJS+w413SpeK06F2klEz6K6POQnfWzsbJqOnItGu5CkGqDIq5DwVjKxIoI/3QjRRnDsnnYVHE4OppkXiRMuiMD4+VA0cW9gGGvC2biU+rZu1nRobOvMYedMwAEUVN1gQqHOzIhFVXEYZeoiCB55qjAhxPHruCi9aBgGTz31FO9///vZvn07GzZs4Otf//p+j3cch/Hjx/PUU0+NqGASoLq6mpUrV/LII4/w2GOPsWrVKlpbW3Ech0gkwowZM7j00ku57rrr8Pv9w91dIcQhKA15WDhvGss0A72+htruIFuzIQwzRTiXRMPGQqXbKcZUvIQ8DtPCGpGqyVw0eyJdqRxv7vCTDFZAVy3EWwBwUNgY8/JO3MOMriRndD+Or7gKxl8AVoaWN59geZ1KW9QklU3RbAVJKGEsR0NTLPzpJIlUjC1tOsVNKRZ0P0LpaR+GyPhhfseEECeKIR2h3CORSHDHHXfwf//3f/stq1hQUMBnPvMZbrvtNgKBwNB2UBwWGaEUJ5qWaJrl65toa28h1dFIc1ecRE7BchQ0xcGvO5QVBvEWllNcVMqC6eWUhjwAZHM2q2s7eau2k0wiCp07IdXZp32XajMrnGSOv4Wm9i6WdU+gI56iNldIVAli6Bphw0ZTHCxHodtUMXMWISfGGL2TSDjIwmqb6nM+JiOVQog+Busze1gCyj2y2SxvvfUW69ev70kLFIlEmD59OnPmzMHlcg1X18RhkIBSnIgcx6G+M8Wa+i62NXVj5zL5Gt6qhqq7mVhewIyqMFWF3n7XiqdNi7d2dvJ2XRfZWDt07njPjm8Hq7uJjoyCZsbZqozB6wtQ6bMoNCzUXk3aDnRkNRrTBql0msl6C6WFIa6Y4qb0vE/JmkohRI/jIqB88cUXATj99NPxer2HdE46nWbFihUAnHfeeYPWN3HkJKAUJ7pMziKezmFaDoamEPDouHXtkM5NZnOs2N7B2rourFhLfuNNLgNmmrVtDl2mSoftp9JIMsfXjObdWyP8vWwHtsTddCdSTPV2MrYkzDWLLkSJjB3gOxZCjFTHxRrK+fPno6oqa9euZerUqYd0zq5du3rOO5RE4UIIMdTcuoY7cGgB5Hv5XDrzTypl1phCXn83xKZdxTixRqI715K0PXRbLhRVwUJlfbqYKitKJBNH8Rb02V0OoCowKZBhg6lTm/IQSDnUb1nN6LljB+ZGhRBiP4Y0DyXkp4mG8jwhhBgJwl6DBdPKueascUwYN54mijDRyWAQVE0UZU85xyLWpwrpjsUg3gRmuk87qgIVfoWo7SGVSrJ2e5PkqRRCDLohDygPl23nU2to2pH99i+EECNJccDNggleRnlNHEVF1XS8Rt81kAnbxaZMMRsTIeLRDki09tQQB4i4LAxdpTmtszXmIpPoHurbEEKcYI75gHLnzp3A3rKNQghxvIsnU/h1h4iWZoI3TsDr2adGOEDUdrM+U0pN3EOyuzVfH9zOb9gJ6zkSto6NQjyZGqY7EUKcKAZ1DWVtbW2/X29sbDxoKqBMJsO2bdv49re/3ZMEXQghTgQmBgC2ohLSsowNqXSYGnVJL2nThlyqT2L0DstLh+WhJJekKt2M2+dHw4W1+0f8nvaEEGKwDGpAuafKS2+O43DhhRcedlvXXXfdQHRJCCGOeYYvCChoqoaVy6DgUOSyiBgpWrM69Uk/WTMHuTT5ajkACq05P205H2VmgowZQ3OFe7UnhBCDZ1ADyv1tpDmcDTYej4cvf/nLfOpTnxqobgkhxDEt4Peh+iP44120xwzsbBLV7UdRoNSdo8iVozlj0JAKkMtmwcqyJ7B0UGg0vTSbRVQpSexMjECmBZCgUggxeAY1oHzggQf6/P2GG25AURTuvPNORo0atd/zFEXB4/FQUVHBrFmzpFKOEOKE4tY1JowdSyK6iqa4m45kkmLX3hRBmgKVHpNSl0ljxqAx5cc2dweWDqQslRwaKQzqsm7Wv7KMmeMq0CdeAIGSYb47IcTxaEgTm6uqiqIorFu37pDzUIpjnyQ2F2Lg1bUneGzpUja2ZrHSMaYF4qj+4n6r3mRthYaUQXNKwUrHaDU9qKpGiSvD1GKdkGET1C3mFSWYOnky6rjzwC2/qAtxIjouEps/99xzQP9rK4UQQuxVFfFRXD2VMem32ZgLsyVhMcluQvWE9klo7lJsxhqdlJkJViol5HQfJVoCn8dDSM8CEMtpPNMcYlVnPWe/ez/jT56FMnou6AcvcZtJp4hHuzDNDIbhJhAqwO05tGpnQogTw5AGlOeff/5QXk4IIUYsRVFYMGcyi5NpJjvvUNNdxIa0m4psJxG9E9XlIZ/5zcbOpunIuWi0C/F4PJwVyJL0T6DcZ0Oirk+OyvaszhO7Aoxq38A5leuonHo2lJ0Cat8sco5tU79zK2s2bmRbYwd2r7ksVYEJFRFmTp1KVfVEFPWYz0AnhBhkQxpQCiGEOHSlIQ8L501jmWag19dQ2x1kazaEYaYI55Jo2FiodDvFmIqXkMdhalgjUjWZhadPxqWrvPzOWOp3boHuXX1SDe1Ku/jzuzCh+RXOHvMWRVPnQ2Q8AC0NdSx/4UXa4hlSWYvmhEXCBMvJr9/0G5DItLKl4WWKAytZcP55lFaOHqZ3SQhxLBjSNZS9OY7D22+/zZo1a2hrayOVSh109/dtt902RL0Th0PWUAoxuFqiaZavb6KtvYVURyPNXXESOQXLUdAUB7/uUFYYxFtYTnFRKQuml1Ma8gD5n7U725O8vLme1rqtEG/ep30Fh2mhNPMmFNPhHcOyNzbTkTSpjdpETRVDcQi7bTTAArozKqajEDJsxoRUIj6DheefSfWEKUP7xgghDttgfWYPS0D5+9//nu985zs9VXAOlWVZg9QjcTQkoBRi8DmOQ31nijX1XWxr6sbOZcC2QNVQdTcTywuYURWmqtCL0s/GHcdx2NwU49WNO4g2boNU5z7HpLMmHdEYbhW2pQN4daj0Q6HXQFX3tmnbDh1Jk8YkpHIKkwuhNODiios+ICOVQhzjjotNOQDf+ta3+P73v39IuSgVRTmsnJVCCHG8UhSF0REfoyM+MlPLiKdzmJaDoSkEPDpuXTvo+SdXhJhUOp21u0azYv0WUq3vQjaZP8BxqOl06DLDtJsuKvQYJxdk0b2hfXaWq6pCccBFxOewpcOkplNBV02Wv/Ai13zs47KmUogT0JB+17/xxhv893//NwD/9m//xttvv82qVauA/A87y7JobW3lqaee4sMf/jCO43DOOefQ2NiIbdsHaloIIU4Ybl2jKOCmPOyhKOA+aDDZm66pzB5TyA0XzmHuWe/HKJ0Emoto2iJpqXTnDFQFHNXF2m4PrZ1dOJkE9PPLvaoqTIoYeHWH2qhNWzxDfe22gbxVIcQIMaQB5X333QdAdXU1Tz75JDNmzMAw9taYVRSFoqIiFixYwF//+ld++ctf8vLLL3PRRReRzWaHsqtCCHFcc+saZ00q4fqLzmTGGRfQYvkwbYWMoxHUTBQFso7OtnSItZ0qnV2dOGZmn3ZUVaHCB1FTJZW1WLtxwzDcjRBiuA1pQPnqq6+iKApf/vKX0fWDz7Z/4Qtf4KMf/Shr167l3nvvHYIeCiHEiSXg1jlnYoTRPhNVN1AVBa/ad0YoZRu8kwqxsT1HLNoFVq7P6xGfgaE4NCcstjZ0kEmnhvAOhBDHgiENKBsbGwGYNm3a3g70WmtjmuY+51x77bU4jsOf//znwe+gEEKcgOLRLtyqQ4GaZlIgSdjvAXXfX/pjtpsN8QA1bSlS8WhPGiJVVQi7bRIm2E6+PSHEiWVIA8o9AWNpaWnP13rX6W5tbd3nnD27j7Zu3TrIvRNCiBOTuXsq23IgoFqcHM5xUkTF53GDsu/HRIflZW3Uy7ttCbKpODhOPqWQ07c9IcSJY0gDypKSEgCi0WjP18rKytC0/ILyTZs27XPOnlHNWCw2BD0UQogTj2G4gXzScov8pu5Cl80phRYTCg1cLhfQd6e3g0KL6eXtThe17TGyOQtN6dueEOLEMaQB5Z6p7s2bN/d8zeVy9Xy9v2ntBx98EIDKysoh6KEQQpx4AqEC1N0VcLozKvbuOouKAiUei1OLbKoLDHTd2OdcG5VdGR+bE0EyOQssk0CoYIjvQAgx3IY0oDz33HNxHIfnnnuuz9c/9rGP4TgO999/P7fffjsbNmxgxYoVfPGLX+TRRx9FURQ++MEPDmVXhRDihOH2eJlQEaHMr2E6Ch3JvuvZVQUqfDanFjtUhlyoWt/1lSlbxUQlYbvYFTXZ8fbzOJn4UN6CEGKYDWmlnA0bNnDKKacQCASor68nFAoBkEwmmT59Ojt27NinwoPjOEQiEd5++22pwHKMkko5Qox8ddtreOyfL7OxNYvlwLTivtVxesvaCvUxaE1ZWJZNq+lGxaHElWVqxCHkMSj32Zw7cwpVJ88DbchraAgh9mOwPrOHfMr7ueee4y9/+Qu53N60Ez6fj+eee46zzz4bx3H6PKZPn86zzz4rQYoQQgyiquqJFAfcjAmppHIKWzrMnqnv93KpDuPDDtOLFLJo5ByVsG7i02xC7nzw2JRUWfxaDX9b8kfad6zvNzG6EOL4MSy1vA/knXfeYcOGDeRyOSZNmsSsWbOGu0viIGSEUojjQ0tDHYuf/ict8Sw1neDVHSp8+TyTB6rlPSqoEjcVqgLgd+07TqHgML3Cx7wzziZQOuaQ+pJJp4hHuzDNDIbhJhAqwO3xDti9CnGiGqzP7GMuoBQjjwSUQhw/dm7bzLIXXqMjaVIbtYmaKobiEHbb+dRA5DfumI5CyLAZE1KJ+AwuPm8eOXcBL698k872fVPAAbhUm9njSplzxrm4AoX7vO7YNvU7t7Jm40a2NXbQe4BUVWBCRYSZU6dSVT1R6oULcYQG6zN70Ba2rFmzhpkzZw5W80IIIQZB9YQpXOH1s/yFFwm4M6SyFs0Jm4SpYDn51EJFHocyv4rX5aI44GbB+edRWpn/gBo3aiHra7bx+urVJON9071lbZXXt7WxbudfmDe1mumzzkZ1eYD86OjyF16kLb7nmhYJk55r+g1IZFrZ0vAyxYGVfa4phBh+gzZCqWkao0eP5uKLL2bRokW8733v253LTBxvZIRSiOOPY9vU125jzYYN/Y4WTqyMMGPqNKrGTOh3tDBj5nhrzTpWbdiIme0/0XnEq3L2qdPQ3T6efGnFYY+KLjz/TKonTBmcN0CI49SIm/LeU1Jxz65tn8/HBz7wARYtWsTFF19MWVnZYFxWDAMJKIU4vh3NesZ4Ms1rK1ewYet2HNva5/VEJkd9NEfApbAroeLVHSr9UOg98LrNyYVQGnBxxUUfkJFKIQ7DiAsoGxoaWLZsGUuXLuXZZ58lnU7nL6goKIrCnDlzWLRoEYsWLZKp8RFOAkohxMG0dXTxyhuv8259I7D7Y8dxWNti0mVqtJoeio0Up5eAz73/1Vi27bClw6Q7qzC1SGFskZ9rPvZxWVMpxCEacQFlb6lUin/+858sW7aMZcuW9ZRT3DN6WVVV1TM1/v73v1+mxkcYCSiFEIeqrr6el1asoLm9i2gqx8ZOaM26sFEoNTKoikOZF0aFNAyt/yDRth02tJloCkwtcXH5v53L6LGThvhOhBiZRnRA+V5vvfUWS5cuZdmyZaxatSrfEZkaH7EkoBRCHA7HcXinZhP/u/QltsdUmk0PET2DT7N7jtEUqPRDeUBD6yfBels8y9aoysxihZnjSrj4Q5cO4R0IMXIdF4nN95gzZw533HEHb775JvX19fzqV7/iQx/6EB6Ph0Qiwd/+9jduvPFGRo0axdy5c7nrrrtYs2bNcHRVCCHEAFMUhXHV46gKGfg1G0Ox8ap2n2MsB+risKbVojVp7ZMXPeIzMBSH5oTF1oYOMunUEN6BEOK9hn3RSWVlJZ/97GdZunQp7e3tPPHEE9x4442Ul5dj2zYrV67k9ttvZ/bs2YwZM4YvfelLrFu3bri7LYQQ4ijEo12ggFtzmBJIUumzUNl3wixrwbYuh3VtFl2Zva+rqkLYbZMwwXZ2tyeEGDbHVIFVj8fDwoULWbhwIZCfGt+zsWfVqlU9o5llZWWccsopw9xbIYQQR8o086mELAd8KlQXuin356iL5mjL7vvRlDQdNrdbhN0qY0IqfoN8SiGnb3tCiOFxTAWU7zVnzhzmzJnD7bffTkNDA0uXLuXJJ5/E5/MNd9eEEEIcBcNwA/m1knuSCbldOhOLdSrSWXZGbaK5fT+iujM269ocSrwKWVtBU/q2J4QYHoM25X3LLbfw9ttvD1h7lZWVfO5zn+OJJ57ga1/72oC1K4QQYugFQgWouyvgdGdU7F6Z0/0eFycXuzkplMOr7pu7EsehJWHzTtxH0tZxdrcnhBg+gxZQ/s///A9z5sxhxowZ/OhHP+pJFSSEEEK4PV4mVEQo82uYjkJH0uzzuqIqFAY8zCjRGe/P4FL6BpYpWyXnKKRzDnVxhS0N7X2CUiHE0BrUTTmO47Bhwwa++c1vMmbMGBYsWMDDDz9MKiW78YQQ4kQ3c+pUvC6NkGHTmKTfgFDRNErDfmaWqIz2pNEUG9uBmGXgViwM1aFAz/LMv/7JQ39/lp3NXUN/I0KIwQsoly9fzjXXXIPP58NxHCzL4p///CfXXnst5eXlfOpTn+K5554brMsLIYQ4xlVVT6Q44GZMSCWVU9jSYe53lFHTDUZFApxSaGHZNjlHIayb+DSbkFsHx6atsZYlTy7jL8+9TlssfVh9yeQs2uMZmrrTtMczZHL9TLULIfZr0BObJ5NJlixZwoMPPsizzz6LbedzjfWuknPNNddw7bXXMmXKlMHsihgkkthcCHGkWhrqWPz0P2mJZ6npBK/uUOHL55k8UC3vMd4scdOhIuzB30+pRsUdYPrUaZx5ykn9vg75WbT6zhRr6rvY1tSNncuAbYGqoepuJpSHmVlVQFWht+czS4iR7riolNPY2MhDDz3EH//4R9auXbu3E7u/UefMmcMnP/lJrrrqKoqKioaqW+IoSUAphDgaO7dtZtkLr9GRNKmN2kRNFUNxCLvtfGog8ht3TEchZNiMCalEfAYLz56FEmvmhY21tGW0ftt2hYo5beapzJ5U1aeUY0s0zfL1TbS1t5DqaKS5K04ip2A5Cpri4NcdygoCeCMVFBeVsmB6OaUhz9C8IUIMouMioOxt3bp1/OEPf+BPf/oTDQ0N+c7sDiwNw+Ciiy7iuuuuY9GiRRiGMRxdFIdIAkohxNFqaahj+Qsv0hbPkMpaNCcsEmY+z6S2ezd4mV/D69IoDrhZcP55lFbmf+7YsRY2vvk8r+3oJp7rJ7BUVIIlVZw1exYnV0Wo7UiybGUNHfU11HZbRLMKhpMirCTRsLFQ6XZ8mIqXkMthTFgjUjWZhadPprrIP8TvjBAD67gLKPdwHIdnn32WBx98kCVLlpBIJPId2x1cFhYW8rGPfYxrr72WefPmDWdXxX5IQCmEGAiObVNfu401GzawrbGD3sspVQUmVkaYMXUaVWMmoKjv2QLgOGRbtvLWyld4q8nCdPqZotZceEvH0ZFRyLRso6ZbxWvFqVQ7KdSzqC4P+a0FNnY2TUfORaNdSEoLMDlsUzrmJK44d4aMVIoR7bgNKHvrvd7yX//6F5a1d1G0qqrkcrlh7J3YHwkohRADLZNOEY92YZoZDMNNIFSA2+M9+Im2RXz7Sl5dtZaNXRoOvQJLB9Z2uehK5YjjZrTazjRfN6onBIYPeq+TdBwwk9jpKFtSQbq1CFMLbcaedCrXzD9F1lSKEWuwPrOHvZZ3bz6fj2uuuYbly5fz9ttvM23atJ5v2mMo7hVCCDHI3B4vRaUVlI8aS1FpxaEFkwCqRmDCPC685Bo+cfooxviyPS9FcyrJdJpuU8PMZEhYOjuVKkzd3zeYhPzfXX7UYDmT/Cm8uW5qY9C2cyP1nckBvFMhjg/HVEBpmiZLlizhsssuY86cOWzcuHG4uySEEGIkcvkoPfUiLvvwZVw6xU+RK0dTEkwbMrZCUM0AKs2xLG+3qzSkdPrNWKQoqP5iKtxpoukcqXSKte9sG+q7EeKYd0zU8n7llVd48MEHWbx4MV1dXcDeEclgMMjll1/OJz/5yWHsoRBCiJFICZYy7pwrKZ/4Dpsefpadtg8VG69mg6IBDpaZpbZLpdnlYnTAochl9R2wVBQiPhdGOkNzysfWHdvJnD4Nt97/znIhTkTDFlBu2bKFBx98kIceeogdO3YAe4NITdP4wAc+wHXXXcdHPvIRPB5ZAC2EEOIIKQpJ3yhKQ17Kk0mCjo2qurB7H+PYZDJptpo6TR6D6oBFUN97hOryEdYSJNImdryDeCKJOxwc8lsR4lg1pAFlW1sbjzzyCA8++CBvvvkm0Hdt5CmnnMJ1113H1VdfTXl5+VB2TQghxHHMTMYAcFSdMn+OSnecuoRGm+Xre6CdI57MsSHjJuJzMcZn4tEcUBQ0XcfKWYCab08CSiF6DHpAmclk+Nvf/sYf//hHli9f3rNTe08gWVZWxic+8Qmuu+46Zs6cOdjdEUIIcQIyMAHyeSYVHXegkImeDOXxLnamvcRsd98TrAwdMYXOtIdyH4zymlhoaLvHNfe0J4TIG7SA8vnnn+ePf/wjjz/+ONFoFNgbRHo8Hj784Q9z3XXXsWDBAjRN1qEIIYQYPAGfFxUHv5qjPefDdkDV3QTCLqZ6E3TGO6nNBkk7vT8WHRwzRWNUoyXtJpHVqda7UXER8B3irnMhThCDFlC+733vQ1GUniBSURTOOeccrrvuOq688kpCodBgXVoIIYTow+0PMyGQJRHP0RS16ci6KHZboCgo7gARl4+CVJSWhEV9LkjO6ZUExbGIJbN0WG78qo9ZLhu3Pzx8NyPEMWhQp7wdx2HChAlce+21XHvttYwbN24wLyeEEEL0T3czc1wZW7pbCcXTNCYMIq58BR4AFBXVV0C526Q41c2upEZTLoCDgu1AzDZwO1msXI7ahIe/rGnm3JPKKA64D3hZIU4UgxZQfvazn+W6667jrLPOGqxLCCGEEIesavIsit95hjHeNBtTXrbEvUwKZPYGlQCagR4optqdoizRwc60jy3ZCDlboURL41NtQiTYsfZlaneN45RJ45k3oQif65jIwifEsBm074Bf/epXg9W0EEIIcdiUwrEsGKuxOO1ncq6NmmQpG2wPFR6TiMvqE1jaupe4N0DWsgibCdxaBkVzMcEbB6MYcmns5k2siTayuW4ic0+q4tTRhWjqoZVkzOQs4ukcpuVgaAoBjy55LcWINqy/Um3bto3XXnuNpqYmkskkX/ziFykuLh7OLgkhhDheKQqlsy9mYfLPLHP86N1N1GYK2ZoNYuguwoaNpjhYjkK3qWLmLEJOklm+dgpdNlNczWx3TyXeuz54qovMzrd4sbOBtTsncO6UCiaU+Put9e04DvWdKdbUd7GtJYHdK22eqihMKPUzs6qAqkKv1AoXI86wBJSrVq3iK1/5Cq+88kqfr19++eV9Aspf/vKXfOc73yEcDrNx40YMwxjqrgohhDieBMupnruIK7QnWF4XIBDtIpVtozkXJGH5sBwVTbEpcpKUaTG8Lp3iUIgFo21Kp1xItrmGN3d2sarLj+nsCfociDbQFW9haVs1VaPHcf6UUkqDe4tytETTLN/QRFs8Syqbo7k7SSKdwbIsNE3D73GTyJhsaY5THHCxYFo5pSEp6iFGDsXpnVl8CCxbtowrrriCbDbbJ6m5oiisW7eOqVOn9nwtFotRWVlJMpnkscce4yMf+chQdlUcovr6ekaPHg1AXV0dVVVVw9wjIYQ4iFgTzsal1Ld3s6ZdY1tnDjtnAg6goOoGEyM6MyIWVUVhlKmLIFgOjgMtG4lufp5XG2FTrJ/0QS4/StEEpo0fzVkTimiLZ1i2tpGOeIba5jai0SiGlSCsW3tHRHMapuYnFAoxpqyYSMDNwhkVVBf5h/qdEce5wfrMHtIRysbGRj7+8Y+TyWSYNm0aP/rRjzjnnHMIBvuvNhAMBvnwhz/MI488wlNPPSUBpRBCiIERLEc54zOM7qpl9K63yLRsJW7qmLaCoToEDAd32VionA0FY+gp7q0oUDaNUNEkLqp9jZk1q3ix1UtD2rW37WwCp3Et66ONrHp3DF1ZBT2XYmvtLrxkmORKUqh1olrZfICKgu1y0aEU0tgVZ2NXO5PHVrFsLVwxp0pGKsWIMKQB5U9/+lMSiQTV1dW89NJLFBQUHPSc+fPn86c//Ym33npr8DsohBDixKEoUFgNhdW4cxncmRhYWdBc4A6CfoCUQLoLxp9PRcUMrtzyLDU7anmpPUgs12tjTbyVzY0Jumwfnakco1xJphp1aJk0KBoYHkAFbFQzSbETI6J52GJXUvPuDnRyLN+gc828allTKY55QxpQPv300yiKwq233npIwSTAlClTANi+ffsg9kwIIcQJTXcfOIDcH28hyozLOalqO+Nr/smqXXHe7PKRtVWiOZVk1qY7GQUHTCvOplwBY4MOAa9776gn5EcqzSRqJsokezsbnHHU1tYS8Hqo7yxldMS3/z4IcQxQD37IwNm5cycAZ5xxxiGfs6eiTjweH5Q+CSGEEEctMg7jjE8xd+5ZfHJ8jGmhFM0ZHTObJmOrBImjOBZxx836ZCFbEx4ydq+AUlHA5YdAOarLS4XdRDRjk2rawtr6rmG7LSEO1ZAGlLlcDgDbtg/5nO7ubgACgcCg9EkIIYQYEKoGVacROPuznH/KeEbpCRzHRsXCq+RANcDOQTZGWyLHmi4vdSkDq/fWWEUBXzERVw7DStIcM9lau4tMzhq22xLiUAxpQFleXg7Au+++e8jnrFixAoAxY8YMSp+EEEKIAeXyEx/zPnyhQiJalnFqMx7doXf6SqwMdjrGrjis6fLSmtHpSXyiKKieEGHiJEwHO9pIPJ0bjjsR4pANaUB57rnn4jgOixcvPqTjs9ksv/71r1EUhfnz5w9u5wbJ008/zVVXXcX48ePx+Xx4PB5Gjx7NJZdcwp///OfDGq09HLZtc+aZZ6IoSs9DCCHE0DCzacgmsDUXBW6Y6etkjNGNpvT+me+AmSKbTrItprM+5iVq7v5YNnxoKli5DCTa8+0JcQwb0oDy+uuvB+CJJ57gmWeeOeCx2WyW6667jm3btqEoCjfeeOMQ9HDgZDIZLr/8cj74wQ/y5z//me3bt5NKpchkMtTX1/PEE09w1VVXMX/+fLq6ugb8+vfeey+vv/76gLcrhBDi4IxcEnDQnByW4UcNV1IZ0DjV00KpniCf73I3x4JsnEQqw8aohy1xNxlbxdLcaHYOcHa3J8Sxa0gDyvnz5/Oxj30Mx3FYtGgR3/zmN3umtAF27NjBq6++yg9/+EOmTZvG4sWLURSFz3/+80ybNm0ou3rUvvzlL/P4448DUFpayo9+9CP+9a9/8dJLL3HvvfdSXV0NwEsvvcRVV101oNfetWsX//Vf/4WiKFLKUgghhkFAt1Bx8KtZunNubFTwFmCEyhjvyzDD00JYe8+oo5WFTIz2pM3b3V52pgN4lSwqDgFd1lCKY9uQV8rJZDJ89KMf5e9///sBp2H3dOuyyy7jz3/+M5qm7ffYY01zczOVlZXYtk1hYSFr167dJxN9NBpl5syZ7NixA4CVK1dy2mmnDcj1P/KRj/DXv/6VT33qU2zbto0XXngBgMH6p5ZKOUII8R6JNpY98Shrd8VZky5hYoFKsbtXUGimcJKddOU0arMhUk7f0sJJx01HzsNod5z3j7L5zJWXogRKhvgmxPFosD6zh3SEEsDtdrNs2TJ+/etfM378eBzH6fdRVVXFvffey2OPPTaigkmAN954o2dt5A033NDvP1YoFOKWW27p+ftrr702INdesmQJf/3rXykuLubuu+8ekDaFEEIcJneQmeE0XpdKiCSNaQO79+/0hhclVEGh38Mpnjaqja6e9ZW2A7GchttO4Vg5GlIaizfEaI7KOkpx7BrSxOa93Xjjjdx4441s3LiRN998k5aWFizLoqioiFmzZjF79uwRu5Ekm832PB8/fvx+j5swYUK/5xypaDTKTTfdBMAPf/hDioqKjrpNIYQQR0B3UzV6LMVNDYxJdrHRDLEl7mZSIIO656NNUcATRnX5qUh1UZxtpjYbZHO2mJyjUKIm8ak2oVyUXXU7+FPUZNqoAs6aUITfPWwf30L0a9j/R06dOpWpU6cOdzcG1EknndTz/EApkrZt29bvOUfqP/7jP2hoaOC8887r2QAlhBBieChVs1mwaxuLow6Ts83UmFVsiHqp8GSJuKy9gaWqY/uK6cYimbUIq2nKSOGgMsHdDa4IdO7AiTezPjWemuYYc8dFOHV0Abo25BONQvRL/icOglNOOYWzzjoLgN/97nc0NDTsc0wsFuOee+4B8qOYF1544VFd87XXXuNXv/oVhmFw3333HVVbQgghBkBBNaWRAhZW25SqUabqu9AUh60JD6u7fWyNu9mecLE17mZ1l4+tmRCaN8ip3lbONLbxuaLVVLqze0tCmilo3kC2YQMvbarnwdd3sq01fsjr4zM5i/Z4hqbuNO3xjCRLFwNq2Ecoj1cPPPAAF110Edu3b2f27Nl84xvfYPbs2ei6zvr167n77rvZvn07xcXFPPTQQ7hcriO+lmma3HjjjTiOw9e+9rUBH/Gtr68/4OuNjY0Dej0hhDguKAqcvIjq9B+5gk6W72gh4KRJ+QpptoIkLA3LUdEUhyKXSZkWw2t2UuyKsmCih1JSnO2zWJ2Ks6LTT9bePQaUbIdUJ13xUTyRSFNdHOS8ySUUB/atRe44DvWdKdbUd7GtqRs7lwHbAlVD1d1MKA8zs6qAqkLviF1mJo4Ng7LLe8mSJVx22WUD3WyPhoYGamtrmTdv3qBdYyC0t7dz33338YMf/GCfWuSGYXDzzTdz8803H/UOq7vuuotvf/vbjBs3jg0bNuD1entemz9//lHv8j6cHzKyy1sIId6j411YvwQn0UF9SwtrunxsyxVh617yE4U2ai7FRKONGeEUVaWlKP4ITL8MNDds+QfxzhZeaQ+wMebt27buhsh4VH8RM6oKOHNCER4jv5G1JZpm+fom2tpbSHU00twVJ5FTsBwFTXHw6w5lBQG8kQqKi0pZML2c0pBnyN8eMbQGa5f3oIxQXn755cyYMYP/9//+H5dffvmAtVtXV8d///d/88ADD/Cf//mfx3xAuXTpUh566KF9gknIjyo++uijlJSU8PWvf/2IfzPcsmUL/9//9/8B8Itf/KJPMCmEEOIYEBkPs65B2bSU0Z4goyuSZLp3EU9lMG0wVAh43bjDZWBUgr8YTl4EwXy5YuZcT6BxDQvefZ6ZsSTPt4VoTO9OM5TLQMsmbG8Bb2fHs7kpxpkTigh7df7+5hY66muo7baIZhUMJ0NYSaJhY6HSnvHRlFQJtW1lTHg7i6OTWXj6ZKqL/MP3XokRa1ACygkTJrB27Vo+9rGPMWbMGD7xiU/wiU984oiSkycSCf7yl7/w8MMP889//pNcLoeu6312SB+pgRjef+CBB/rdAHPrrbfyk5/8BIBLL72Ur3/968ycORNN09i0aRM///nPeeCBB/jmN7/JG2+8waOPPnpE6ZE+97nPkU6n+ehHP8qHPvSho72dftXV1R3w9cbGRs4444xBubYQQhwXguVw+megqxZ2vYXbtQW306sMo6JCyWSonA0FY/LT5T2vKVB5KpScRPmOl/mY5y02x9y80h4gltv9uZHqgobVpEOVLIsl2dHUQShVT2MSvFacSWonhUYW1eVhz6ione2gI+eiMV3IRjPAZHM9yyyTK86dISOV4rANypS3aZrcc8893H333bS3t/cEbpMmTWLevHmcfvrpzJo1i9LSUgoLCyksLCSVStHR0UFnZyc1NTWsXLmSFStWsGLFCtLpdJ9E59/73veYPHnyUfdzsALKJ598koULFwL5cpMPPPBAv+fedttt3HnnnQD87Gc/60n5c6juv/9+Pv3pTxMMBtm0aROjRo3a55iBmPI+GElsLoQQhymXgUwsXx1Hc4E7uHfzzcHEW2DLP8h21PNmp4+3uvzknN2fZw6sjXrpyji0Zl0UEeWMQAs+nx8MX99A1XHATGKno2xJBenWIkwttBl70qlcM/8UWVN5nBqsz+xBrZQTj8e59957+eUvf9kzynU4/0H3dM3tdnPZZZdx8803D+hI2ObNm4+6jYqKCsLhcJ+v7alUoygKdXV1/QZ6AOl0mpKSEuLxOKeeeiqrV68+rGufdNJJ1NTUsHDhQq6++up+j/nud7/Lpk2bAPjTn/4EgN/vZ9GiRYd1rQORgFIIIYaY40DLRtj2L7rjSV5uD1IT9xA1VTa25WjNurEdm1J3DtXtp9yTY5Q3i97fR7DjYCfa2BAPoHmCTC1xcfmHFzE6IlPfx6MRGVDuYds2zzzzDI8++ijPPfdcT7nBA/F4PMydO5dLLrmE6667jkgkMtjdHDBTpkzhnXfeoaysjKampgMeO2/ePN544w08Hg+pVOqwrjN27Fh27tx52P2rrq4+pH+DQyUBpRBCDJNcFna+AvUrqU+o/LymgC1d0Jz1EFET+AwlPwKqezA0GO3NUuLKsc/YjuPQ1tXF1kwhM0sUZs6cw8VnzhiOOxKDbERtynkvVVVZsGABCxYsAGDXrl28+uqr1NfX09raSkdHBx6Ph5KSEkpKSjjllFM47bTTMAzjIC0fm3Q9/7bmcrmDHmuaZp9zhBBCiEOmu2DCBVAxk5J3/knFO+/SpAToUHS8mg1o+Wl1y8TUPbxruWk2DKq9GUJG7zWcChGfCyOdoTnlY+uO7WROn4ZbH1mlj8XwGZYoZtSoUVxxxRXDcekhsSd9T3t7O5s2beLkk0/u97iOjg7Wr1/fc87hOpRRxqFYQymEEGKY+SLEx12E89qDGJhM0ZrwuAyacgEcFMCBXAqsLAnHy8aclyJXjjHeLG4t/9mgunyEtQSJtIkd7yCeSOIOB4f3vsSIIZVyBkHv9Ylf+cpX+q3Tbds2X/7yl3te27OJp7cdO3agKAqKojB//vxB668QQoiRz0zFwfBiaV5cbjfVrjgzPC0UaOm9BzkWZONgpmjPaKyJ+tiVMrAdQFHQdB3LtgAHMxkbrlsRI5AElIPg+uuv7xmV/Mc//sFpp53G//7v/7JixQreeustHnzwQc455xweeughAMrKyvjqV786nF0WQggxwhnkl1Bpio2l+yBUgdftYoq7nSnuNryKufdgKwuZGLaZpS7pYk23l46sRs7R0LD7tCfEoZCFe4PA5XLx1FNPcckll7BmzRrWrVvHZz/72X6PHTduHEuWLKG4uHiIeymEEOJ4EvB5UXHwqznacz5sRUH1l4CZoiDVSUhtoSXnpy4XwnJUek+DZwwv78Q8dKV1xhtdqDgEfFIoQxw6GaEcJNXV1axcuZI//OEPfPjDH6aqqgq3243L5aK8vJwLL7yQe++9l3Xr1nHqqacOd3eFEEKMcG5/mAmBLGWeHGbOpiO7e0ON4YVgBaq3gHIjxameZkr1BLB7Xf3uafBUJksip9FqB8g6Ko4rMGz3IkaeIUkbJI5vkjZICCGODXWvL+Gxt1vZ2JrFcgWZVmij9k4RZOcg1QlmkoRtsCMbJma7sR1ozXlR7RwlPpWpowooHz+dsycWM60yJEnOjyOD9ZktI5RCCCHEcaJq8iyKvQpjvGlS2Rxb4vlgsYeqg78E/KX4dZjqbmOC0UHUdpOzFcJaGp8dJ2R1kYx18czGZh5ZWUdj9+HlSRYnHgkohRBCiOOEUjiWBWM1IkE/k402upMZNkQ9tGW0voGl4cUOVNCul9JkBSlwokzVd+HTHSZ44+DkoHENtG+lqTPGIyvqWL6hiUTm4PmV98jkLNrjGZq607THM2Ry1sDfsDhmyKYcIYQQ4nihKJTOvpiFyT+zzPGjdzdRmylkazaIobsIGzaa4mA5Ct2miplzE1K6me5uJmLkOC+0me3+U9iW2T3FHWuCRBsUjmVjg8PWljjzxkc4dXQhmrrvNLjjONR3plhT38W2lgR2r1V1qqIwodTPzKoCqgq9Mo1+nBm0gHL9+vVMmzZN/sMIIYQQQylYTvXcRVyhPcHyugCBaBepbBvNuSAJy4flqGiKTZGTpEyL4fXqFIdKWVDcTmloIqeQYUeikxfagnSYen7dZftWiDeTjYznxRqbdfXdzD+plLHFe+t9t0TTLN/QRFs8Syqbo7k7SSKdwbIsNE3D73GTyJhsaY5THHCxYFo5pSHPML5RYiAN2qYcVVXx+/28/fbbTJgwYTAuIY4RsilHCCGOQbEmnI1LqW/vZk27xrbOHHbOJL+7W0HVDSZGdGZELKqKwihTF4GvGOpXwM5XsHI53u728XqHn6zda4VcsBwKq0E1GF/i5/zJJXSnzP+fvfuOb6s+Fz/+OdrDsmVbcmzZibNj4jhxIAkpoaxCoUAoG8oMbaGlhdvyK6X30tsLlN6WlpbLvW2BcttSIEAh7HmhUBJGgExnODh7eUsesizJmuf3h6tTKx5xhuWR5/16+RVbZ32PHUmPvuN5eH1jA62dEfY1+ejo6MCYCJJjSPyzRzSuJ6a3k52dzYRxLvKyzJw/u4jSfHt/dyCGwKis5R0KhYhEIkN5CSGEEEL0xVGIsuCbjG/fx/i6tUSad9AZMxBLKhh1KllGFfO4ieA5HpwTIDWiWHoSjCtHv+M9TlC2UpbVxUctWWwJ/CMvZY9h8F2MY0t9B22hKKZkFzv31WElwjRTiFx9G7pEFNTuADZpMtGq5NLQ3smW9hamTyzh9Y1w2Qkl0lM5BgzbHMpdu3bxwx/+kAULFvCjH/1ouJohhBBCjF2K0t2bmFuKOR7BHAl0V8nRm8DsAIO57+MsOTDrYmjdhX373zjb0MrsnDDLvQ4aI8Z/DoMHmtgWLaQ9FKW13U+JOcRM4370kS5Q9GC00L3+N4kuFsKlBsjTW9ie9LBt1x4MxHm72sA1C0tlitwoN2yrvMPhMC+99BJ33nnncDVBCCGEOHYYzGB3Qban+9/+gsme8ibDvG/A5FMpsqtcWdLKWQV+bPru8owdwSAh3378rV4UNU6sK0RNl5OgeRxkF3cPodvyuv/9x886kkxL7saqhtm3bx++1lZq2yQt0WgnaYOEEEII0T+9oXsYfMFNKAUzmJXdxfUTfBzvDNIcMRCLdhGJJ3HE21AUhYA+l03hfHaFzMSSPc6jKGCyQ1YhOpOVomQjHZEk4cbtbKxtH667E0eJBJRCCCGEOLjUMPicK7A4clmYF6TEGAA1iY4EViXWPV8yGoRkguaIkQ1+G41dBtKW/yoK2FzkmeIYEyGaAjF27KuTPJWjnASUQgghhBi8fwyDdxZ/EUuik1wlxGRdExaDCgpabXBiYeJJ2BMys6nDSkesR8ihKOgs2eTQSTCmkuxooLNr8EnTxcgjAaUQQgghDo3eQKxgFlicJBUdOboIcyw+xhs70PGPce5EFP6xCCgU17ElYGV7p5lI8h+Lb4w29DpIxCMQbCEW7Rq++xFHbMgDSlm1JYQQQow9xngIdHr0egMJSzY6g5FiY4A5lmby9aF/7KVCLAyx7mHwlqiBDX4rdWEjSRQSejP6ZBxQu88nRq0hTxt0xhlnMHv2bCoqKrSvmTNnDvVlhRBCCDGEsgwJdKjYdVFaklkks2zooh2YuzqYZm5jXCLInpiTUPIfaYainaA3kzSY2R820RwxEIzqKTH40WEkyyBzKEezIQ0oVVWlqamJd999l3fffVd7XKfTUVRUpP28fPly5syZQ25u7lA2RwghhBBHidlsYUpWhKC/i8YuB60xPS5LTvdK7nAb2YSo0DXTlLBTG8smruogEYFEDIwW2uJmWqN6cnRhPNYoZrMkNx/NhiygfPjhh6mqqqKqqopNmzYRCv2zKzuRSFBXV6cNh3/pS18CYPz48VRWVlJZWcncuXOprKyktLR0qJoohBBCiMNldjAnp4vtXh3ZXSEaunLJMyXQ6Qxgd0MshBJuo1AJkq8Psz+WTXPcBiRJRkMEknrMJAiqFrYG4NP6OCdMTmLUy/KO0WjIann3pKoq27Zt0wLM1FdTU1PvBh0w59LpdDJnzhzmzp3Lb37zm6FuqjgMUstbCCGOTeqmF1m6up49zX62qBPIMeuYlhVBl3orV5PQ1QGRDkClM2FkV9TJvlgOkaQOt+LHaTMxe4ILxs0kx2rk1BluJrvssgZjiAzVe3ZGAsr+NDU19Qoyt2/fTjKZ7LWvoigkEjK/YiSSgFIIIY5RbXto/mwZy2oiNEctbFNLsOpViizR7t7KVEyYiJEMtdIaUaiPZdGSsGJMRjArCWba/djzimBcOVjzAJjksnPqdDe5dtPw3dsYNSYDyr6Ew2E2btyYFmRu2rSJcDgsAeUIJQGlEEIco1QVVv+RvXUNvL4zTquSwz6K6IjrMepUcgwJ9IpKQlXwx3TE4gmyk34mUI9D7aTIBg2KG9VRBChgy4e8SWCwoNcpnFCay/yJeZgMgxsGj8QTdHbFiSVUjHqFLIsBs0E/tL+DUWao3rOHfJX3obJarZx44omceOKJ2mOpIXMhhBBCjCCKAsctprRrKZfRxtt7mslSuwjbcmlKOAgm9CRUHXpFJd8UZ5w1gDXahivewdnZeyiwQnNeLsv9Meq6TBBqgXAbOCeQyPawancrnzd0cOp0N1MLsvocBldVldq2MBtq29nZHCTZo59MpyhMKbAzp8RJSa5VhtGH0IjroRSjj/RQCiHEMa51F2x+ETXYSm1zMxvabeyM55M0WOlOeZ1EFw8z1ehjdk6YkoICFIMJLA6IR1FVqOm08KEvi2DiHz2KRhvkTwaLE4AJeTZOm+EmP8usXba5o4u3qxvxdUYJR+M0+UMEuyIkEgn0ej12i5lxOTasJgOuLBNnlxdSkH1sryY/Zoa8xegjAaUQQggCjfD5axD0QSxExN9EZzhCLAlGHWRZzZhzxnUHinYXHLcYssZB40bY+T7EwkSSCp+12lnfbiPJP3oTswogdyLoTegUhbkTnJw4OY9Gfxevb2ygtTPCviYfHR0dGBPB9GH2uJ6Y3k52djYTxrnIyzJz/uwiSvPtw/qrGk4SUIoRSwJKIYQQQPecyvZ9ULcWfNu7V3mnKDpwTwfP8eCc0D1cnhINwe4V0LABVJWWqJ73vdnsD/9jUY5OD85ScBT94ziV9nAMNRJk+946rETwmELkqm3oElFABRSSehOtSi4NURthzEyfWEJBfh6XnVByzPZUHjNzKIUQQggxSikK5JZ2f8UjWi1v9CYwO8Bg7vs4kw1mfAUKZ8P2t8kPNHGJp43tQTMf+BwE4nQPq3c2Qf4UNnqTtHcE6OjwM8HcSbmhDl2kCxQ9GC1ow+yxEC41QJ7ewvakh2279mAgztvVBq5ZWCpzKo8iCSiFEEIIcfQZzP0HkP3JKYbjl0D9epTdy5meFWGiLcrqNjtr220kokE69lUTCjvxB+Mkk0mC4S72meyUOBwYTNb0nk9VhVgIXaSDacndVKuT2LdvH1lWC7VtBYzPsx3VWz6WSTp6IYQQQowcOh2UnAALvgXjyjHpVBbld3Lt+BYm2iI0RgzEOluIRKI4kn4UnYFGZRwbQi6ao0bSJvIpSncpyKxCdCYrRclGOiJJwo3b2VjbPlx3OCZJQCmEEEKIkcecBTMvgMqrwO4i15TgK+P8FOiDRJMKOuJY1a7uXkg1QUxV2BU0Ux2w0hk/ILxRFLC5yDPFMSZCNAVi7NhXRyQu+a2PFgkohRBCCDFy5ZbCvK/D5NMIqiacyTYKdAGKda3o9TpQExDthFh3cNkZ17G5w8quoIlYz8J7ioLOkk0OnQRjKsmOBjq74sN2W2ONBJRCCCGEGNl0eij9ArHyKwAFVU1SoOtktsVHrj7cvU8iApFOSMRAheaIkQ1+G00Rwz+HwY029DpIxCMQbCEW7RquOxpzJKAUQgghxKhgNOjA7kKvN5DQm7DoE8wwt1Jm9mFR4kASYiGIBUFNElcVdv9jGDwY14GikNCb0SfjgIoxHhruWxozJKAUQgghxKiQZUigQ8VuSOLX55M05QAKTn2E2ZYmxhv96EhCMt7dWxmPgAqdcR2bOqzsDJpojZmx66LoUMkyyBzKo0UCSiGEEEKMCmazhSlZEcYZu4ipCq36/O5k5wYLOgWKjZ3MsTSTpw8DKsS7uudXJrvnSu4NmqmN2tErMMUewWw+NpObDwUJKIUQQggxOpgdzMnpwmrSkU2Ihi4jSZ0R7AVgc4Gix6xLML3nMLiagGiQZDRMIK5gVOPUxbOp7TLhjZmG+47GDAkohRBCCDE6GMyUjJ+IK8vCBEM74ViC7Z3m7rrfJjtke8DkIH0YvAOFJK1RPfFolBwliM1kIGwu4Ok1TSzf2izpg44CCSiFEEIIMWooJcdztidMnkVlur4Jf0xPdYcVX0RPEh3Y8iBrXHe5R8CsxDEqSXQkyVc6sBJhiq4BrDkkVZX1+9p5fOUeaho7UNOyootDIaUXhRBCCDF6OEspyHNyfmkDr+/swKDUsY8idgQtGMMqOYYEesVEQnHgT8SJRWNk67o4xbwNknFsJhM6xQwtO7vnWDonEIzAW5sa2VzXwekz3ORnHWLJSCEBpRBCCCFGEUWB4xZT2rWUy2jj7T3NZKldhG25NCUcBBN6EqoOvaKSb9UzzhbAGqrHlfRxtquBXKueNdaTWNOpEvfXQdAHeVPAlsf+1hBLP93H8aVOTpyUj8kgA7mDJQGlEEIIIUYXRyHMupiCzS9yjaWV2ubdbGhvYmcin6TBSveMviS6eJipRh+zPWFKHCaUsBVyJ/EFa5LjcltY7nOwOwQ0bwFbPuRNJmkws2ZPG1sbA5w63c3UgiwURRnmGx75JKAUQgghxOiTNxnmXoPy+WuMtzgYXxQi4q+jMxwhlgSjDrKsZsw548DoAbsLpp8DrTth/yqcpgRfLWpnZ9DMcp+DQKgFwm3dpR4dHgJdcV7f2EBpvo3TZxSQa5cV4QORgFIIIYQQo5OjEOZ/E9r3Qd1azKbtmNUeBbwVHbing+d4cE7oHi53jodxs2Db/6G072dqVoQJtiir2+ysbbeRaN0Nnc2QPwXM2extCfHkp3uZV5rL/El5GPUyDN4XCSiFEEIIMXopSnevYm5pd2WcSAAS0e5V3mYHGPpYYGN3QeXV0LgJdv4dUyzMovxOjnOEed/rYF8YaNjYHbDmlpLAyGe7W/n8H8PgU9x2GQY/gASUQgghhBgbDOa+A8i+KAoUzYb8qbBrOTRsIM+U4GJPO9uDZlb4HHQGGiHUArmTIMtNRzjGaxvqmey2c9r0AnJsxiG9ndFE+m2FEEIIcewy2aDsXJh7DdhdKApMz4pw3YQWTnAG0SWi4NsGjZshFgJglzfIE5/s4dNdLcQTyYNc4NggAaUQQgghhHM8zPs6TDkd9AbMOpVTXJ1cPb6FYmsUuvxQvx7a94KaIJ5U+WRnC09+upfdvuBwt37YSUAphBBCCAGg08OEhTD/RnBNA8BlTnCZp41zxvmx6+LQvh/q1nevCAfaQzFeXl/Haxvq6eiKDWfrh5UElEIIIYQQPVmdUHEpzLoELNndudQdXVw3oYXKnBBKPAxN1eCtgUQEgB3NnTyxcg+rdreSSB57JRwloBRCCCGE6It7endv5YQTQdFh0auc7g5w1fhWPJZod5WdunXQUQ+qSiyh8vEOH0s/3cu+ltBwtz6jJKAUQgghhOiPwQRTzoB5N0BOMQAF5jiXF7dxVoEfqxKD1l3QsKE7ZRHQGozywrpa3tzUQGckPpytzxgJKIUQQgghDiarAOZeCzO+AkYLigKzsrtYMsHH7OwQSjTQHVS27oRkdxC5tTHA4yv3sG5fG8kxPgwuAaUQQgghxGAoCngqYcFNUFgBgEWv8qWCAF8raaXQHIOOhu5h8JAPUInGk6zY6uWZ1fto8IeHtflDSQJKIYQQQohDYbLDcedD5VVgywdgnCXOFSWtfMndgUXtguYaaPoc4l0ANHdEeHb1ft7d0kRXLDGcrR8SElAKIYQQQhyO3FKY/w2YfCroDOgUmJ0T5voJPmY6whBu7e6t9NeCqqKqsKnOz+Mr91Bd70dVx84wuASUQgghhBCHS6eH0pO6A8u8SQDYDCpnj+vgsuJWXMYotO2Bhipt0U4omuCd6iaWra3F1xkZvrYfRRJQCiGEEEIcKVsezL4CZn61e0gcKLHGuGp8C1/MD2CMdXYv2mn556KdurYwT326jw+3e4nGR3cJR8NwN0AIIYQQYkxQFBg3s7unctcKqF+PXoF5uSGmZ3WxwudgR6ABQi2QNxns+SRVWLOnja2NAU6b4WaKOwtFUYb7Tg6Z9FAKIYQQQhxNRivMOAeOvxbsLgCyjUkWF/n5alEbObpQd5WdHot2Al1xXtvQwKsb6vGHRl8JRwkohRBCCCGGQk4JzPs6TD4N9N2DwpPtUa4d38KJuZ3ou1p6LNrpHvLe5Q3yxCd7+GxXC/HE6BkGl4BSCCGEEGKo6PRQ+gWY/83uYW7AqIOT8oNcM76F8ZauHot2OgCIJ1VW7mzhqc/2sb91dJRwlIBSCCGEEGKoWXNh9uVQfqG2aCfPlOASTxtfGefHnghAw0Zo2aEt2mkNRnl+bS1vbWogOMJLOEpAKYQQQgiRCYoCBcd1V9opPh4UBUWBMkcX101ooTInhBJogLq1EPQC3Xkqtzd3jvhV4BJQCiGEEEJkktEC08/urg2e5Qa6Szie7v5HCUdDCLxboWkLxMPMm5hLrt00zI0emASUQgghhBDDIacYTrgBppyuLdrpWcLRHGkhp3kN85VtkBzZ5RolD6UQQgghxHDR6WHCQnCXwfZ3oGWnVsJxqr2LzoQe494VkD+he9X4CCU9lEIIIYQQw83qhIrLoPwiMGcB3SUcC8xxKJo9ooNJkB5KIYQQQoiRQVGgoAxyJ8LuD6B+HRgsMPn04W7ZQUlAKYQQQggxkhgtMP3LMK4cYmEw2Ya7RQclAaUQQgghxEiUUzzcLRg0mUMphBBCCCGOiASUQgghhBDiiEhAKYQQQgghjogElEIIIYQQ4ohIQCmEEEIIIY6IBJRCCCGEEOKISNogccQ6Ozu179euXUtDQ8MwtkYIIYQQ/amvr9e+7/n+faQkoBRH7NNPP9W+v/DCC4evIUIIIYQYtE8//ZSysrKjci4Z8hZCCCGEEEdEeijFEVu4cKH2/apVqygqKhrG1gghhBCiPw0NDSxYsABIf/8+UhJQiiOWlZWlfV9UVERJSckwtkYIIYQQg9Hz/ftISUApRqx4PE4kEiGRSKDX6zGbzRgM8l9WCCGEGGnk3VmMKKqq0t7eTn19PT6fD1VVtW2KouByufB4PDidThRFGcaWCiGEECJFAkoxYgQCAWpqaggGg0SjUQKBANFolGQyiU6nw2QyEYlE8Hq92O12ysrKcDgcw91sIYQQ4pgnAaUYEVpbW6muriYUCtHW1kZXVxd6vR6r1YqiKKiqSigUIhAIYLFYyM3NpaqqivLycvLy8oa7+UIIIcQxTQJKMewCgQDV1dUEAgG8Xi9GoxG3243NZksb1k4FlX6/n8bGRtxuN9XV1VRWVkpPpRBCCDGMJA+lGFaqqlJTU0MoFMLr9WK1WikqKsJut/eaI6koCna7naKiIqxWK16vl1AoRE1NTdpcSyGEEEJklgSUYli1t7cTDAZpa2vTeiYP7JVMJpNpxyiKgtvtxmg00tbWRjAYxO/3Z7rpQgghhPgHCSjFsKqvrycajdLV1UVOTk6vXslAIKDt05OiKOTk5NDV1UU0GqWuri6TzRZCCCFEDxJQimETj8fx+XwEAgH0ej02my1te1dXF62trcTjcRoaGgiFQmnbbTYber2eQCCAz+cjHo9nsvlCCCGE+AcJKMWwiUQiqKpKNBrVVnOnxONxvF6v9rOqqjQ3N9Pe3q7Nl1QUBavVSjQaRVVVIpFIxu9BCCGEEBJQimGUSCQASCaTfSYp1+v1vR5rb2/H6/Vq8ypTKYV6nk8IIYQQmSUBpRg2qYBRp9P1WqVtMBgoLCzEbrf3Oi4UCtHQ0EAsFkNVVS0Y7SsAFUIIIcTQk4BSDBuz2YyiKJhMJsLhcK+gUqfT4XK5yM3N7XVsLBajvr6ejo4OTCYTiqJgNpsz1XQhhBBC9CABpRg2BoMBl8uFw+EgkUj0WnQD/1zNPW7cOHS69P+ukUiEjo4OVFUlPz8fg0Hy9AshhBDDQQJKMaw8Hg8mkwmLxYLf7+83QXkq4bnJZALQFuEYDAY6OzsJhUKyylsIIYQYJhJQimHldDqx2+3k5uYSi8Xwer39BpVGo5HCwkJsNhuhUIhEIoHFYsFoNBIMBlm7di3BYDDDdyCEEEIICSjFsFIUhbKyMmw2G263m3A4TENDA8FgsFdgqaoq4XCYWCyGwWDAbrdjNBpxuVwoikI4HGbdunU0NzcP090IIYQQxyaZdCaGncPhoLy8nOrqavR6PW1tbXi9XvR6vZafMhVMpnolJ06cCHQv3DEajdq5EokEW7ZsoaOjg8mTJ/eadymEEEKIo08CSjEi5OXlUVlZSU1NDWazmWg0SiAQ0JKWK4qCzWbD4XBgMpmw2+2UlZVhNBqprq4mEAikna+2tpZAIEB5ebk271IIIYQQQ0MCSjFiOBwO5s2bh9/vp66uDp/PlzbsrSgKLpeL4uLitLrfc+fOZdu2bTQ2Nqadz+/3s2bNGsrLy8nJycnovQghhBDHEgkoxYiiKApOpxOn00k8HicSiZBIJNDr9ZjN5j5TA+l0OsrKysjOzmb79u1pQWg0GqWqqoopU6ZQXFzcZ0UeIYQQQhwZCSjFiGUwGA4pt6TH4yErK4vq6uq0ut6qqrJjxw4CgQDTp0+XijpCCCHEUSYrFsSYkp2dzQknnNBndZ2mpibWrVtHOBzu9/h4PE4wGKSjo4NgMCi5LYUQQohBkIBSjDkmk4nZs2czYcKEXttS+Sp9Pp/2mKqqtLW1UV1dzccff8zq1atZt24dq1ev5uOPP6a6upq2trZ+82MKIYaPoigoisLdd989LNefOHEiiqKwZMmSYbl+pi1ZsgRFUbRMG4druP9u4uiTgFKMSYqiMHnyZGbNmtVriDsej7N582Z2795NR0cHa9asYcOGDdpCoIaGBurq6mhoaMDn81FXV8eGDRtYs2ZNr9XkQoxEy5cv196wB/P1l7/8ZbibfMxL/S1OO+204W6KEIdF5lCKMc3lcnHCCSdQXV3dq4pOTU0NnZ2d2O12Ojo66Orq6pX7MhQKEQgEsFgs5ObmUlVVRXl5OXl5ecN0R2K4DXaxmBBCHEvkVVCMeTabjeOPP56tW7dqVXQikQjNzc1Eo1EaGhpwOp243W5sNlvaSvBUUOn3+2lsbMTtdlNdXU1lZSUOh2O4bklkmKqqtLe3U19f3286K4/Hg9PpHHGZBG6++Wa+853vDLhPSUlJhlojhBirJKAUxwS9Xs9xxx1HdnY2O3bswOfzEYvFCAaDGI1GkskkyWSyVzCgKAp2ux2bzYbX69Uq+NTU1DBv3rwRFzyIoy8QCFBTU0MwGExLuJ9MJtHpdJhMJiKRCF6vV0u4P5I+bBQUFDBr1qzhboYQYoyTgFIcMxRFoaSkhEQiwf79+7UhbpvNBkBLSwuRSIS8vLxeJRsVRcHtdtPQ0EBbWxtmsxm/34/T6RyGOxGZ0traSnV1NaFQiLa2NpkWIYQQ/ZBFOeKY09nZicvlAsBsNqf1MnZ2dtLY2EgsFut1nKIo5OTk0NXVRTQapa6uLmNtFpkXCAS0sp6NjY0kk0ncbjclJSW4XC7y8/NxuVyUlJTgdrtJJpM0NjamHTea3H333drCEOiuNHXvvfcyd+5cbSi/r8U7L7/8MpdddhkTJkzAYrHgdDqZN28e99xzD21tbf1e78DVwu3t7fzHf/wH5eXl2O12nE4np5xyCk899dQR3VcymeTmm2/W7u2WW27plbHB7/fzi1/8gkWLFuF2uzGZTBQVFbF48WKef/75EZ/hIZlM8ve//53bb7+dRYsW4XK5MBqNOJ1OKisruf3229m3b9+gzvX555+zZMkSxo8fj8ViYfz48Vx11VWsXr36kNr09NNPc9ppp5Gbm0tWVhazZs3irrvuor29/TDuUIwG0kMpjinxeByfz0coFCI7Oxu73d7rjT81rzI/Px+73Z62zWazodfrCQQC+Hw+4vG4LMgYg1RVpaamhlAohNfrxWq14na7+5ziMBanRWzfvp0vf/nL7Nmzp9992trauPTSS/n73/+e9ngkEmHt2rWsXbuWhx56iFdeeYWFCxcOeL2tW7dyzjnn9Lrehx9+yIcffsgnn3zC7373u0O+j1gsxnXXXcdf//pXAP793/+de++9N22f9957jyuuuIKWlpa0xxsbG3n99dd5/fXXOffcc3n22WfJyso65DZkwk9/+lPuueeeXo/7/X42bNjAhg0bePjhh1m6dCkXXXRRv+d57rnnuO6669IKQ9TW1vLMM8+wbNkyHnnkkYO2JR6Pc9VVV7Fs2bK0x6urq6murmbp0qW8++67h3B3YrSQd0JxTIlEIqiqSjQaxWazkZ+fj9lspqWlJa0XIplM4vV66erqIi8vTwsKFEXBarUSjUZRVZVIJCIB5RjU3t5OMBikra0No9HYbzDZ01iaFnHppZdSV1fHrbfeygUXXEBubi7bt2+ntLQU6H4enXnmmaxbtw69Xs9VV13Fueeey6RJk4jFYnzwwQc88MADNDc3c+6557J+/Xrt2AOFQiEWL15MS0sL//7v/86ZZ55JVlYW69ev55577qG2tpbf//73LF68mLPPPnvQ9xAKhbjkkkv4v//7PxRF4YEHHuD73/9+2j4ff/wxX/nKV4jFYowbN45bb72VOXPm4PF4qK+v59lnn2Xp0qW8+eabXH/99bzwwguH/TsdSvF4nKKiIi666CK+8IUvMHnyZCwWC/v372flypU89NBDdHZ2ctVVV7Fu3TqOO+64XudYvXo1V199NfF4HLPZzG233ca5556L2Wzms88+4+c//zk333wzM2fOHLAtt99+uxZMzpgxgzvuuIPZs2fj9/tZtmwZ//u//8sVV1wxJL8HMbyG7Z3Q6/Wya9cuGhsbtYURTqeTCRMmMHXqVCmPJ4ZEIpEASFuAk5WVhdlsprm5uddQd2oBhtvt1gLH1Ny5nucTY0t9fT3RaJSurq5BBZMpqWkRXq9XmxYx3AFlc3Mzmzdv7nd7QUEBBQUFaY9t3ryZt956iy9/+cvaYyeccIL2/U9/+lPWrVuH0+nk3XffTdsGcPLJJ3P11VfzhS98gYaGBu68885+h65Tv6tPPvmE8vLytOuddtppVFRU0NXVxUMPPTTogLK9vZ3zzz+fjz/+GL1ezx//+MdeicdjsRjXXHMNsViMc845hxdeeEGbTw1w/PHHc/7553PKKadw00038eKLL/K3v/2Ns846a1BtyKRvfvOb3HXXXRiNxrTHjz/+eL761a9y6623snDhQurq6vj5z3/Ok08+2esc3/nOd4jH4xiNRt555x1OOeUUbduCBQu4+OKLWbhwIRs2bOi3HZs2beK3v/2tdu0VK1ak9ep+6Utf4qSTTuL6668/0lsWI1DG5lAGg0Gefvpprr32WiZMmEBhYSEnnXQSF198Mddeey1XXnkl55xzDjNnzsThcLBo0SLuueceNm7cmKkmimNA6oOKTqdL65E0Go0UFRX1OaQViUSor68nFAoB3cOhqQBDPviMPalpEYFAIG3R1mD1NS1iOD388MNUVFT0+/XQQw/1OmbJkiVpwWRPnZ2d/P73vwfg3nvv7RVMppSWlvKTn/wEgGXLlvXKA9vTvffemxZMpkydOpULL7wQgI8++mjA+0xpamritNNO4+OPP8ZsNvP888/3WcXmr3/9K3v27MFisfDEE0/0+3e+8cYbWbBgAcCITQA/ceLEXsFkTyUlJfzwhz8E4NVXX+01J3T16tWsWbMGgG9961tpwWRKcXExv/nNbwZsxyOPPEIymQTg0Ucf7fP19LrrruMrX/nKwDckRqUhDyjXr1/P17/+dQoLC7n22mt5+umnqa2tRVXVfr+6urr45JNP+OlPf8rcuXOZPXs2f/jDH7Q3dCEOV2oRjslkIhwOp72w6nQ68vPzyc/P79UjlUwmaW5uprW1lVAohMlkQlEUzGZzpm9BDLGe0yJSq7kPRV/TIkabq6++ut9tK1aswO/3A91D4wNJBSaxWIy1a9f2uY+iKFx11VX9niMVsLa2th50QceePXs4+eST2bBhA1lZWbz55ptaQHqgV199FYBTTz0Vt9s9qPv45JNPBtxvpOjo6GD37t1UV1ezefNmNm/erAXMqW099ZzTeMMNN/R73osuumjAHvfUeSoqKvr9oAHw9a9/fTC3IUaZIRvyXr9+PT/5yU946623ALQ37sLCQhYsWMAJJ5xAQUEBeXl55ObmEg6HaW1tpa2tjW3btrF69Wo2btxILBZj8+bNfOc73+EnP/kJd9xxB7feequ8kYvDYjAYcLlcRCIRAoEAoVAobeGNoig4HA7MZjNer7fXELjP59OGwF0ul8yfHIP6mhZxqEbStIi77rrrkOslz549u99tqZ4sgKKiokGfs7Gxsc/HUyvm+9Mz/VIgEOg3oPn8889ZtGgR9fX15Ofn8+abb2o9i31J3cfbb7896L9zf/fQl/b2dmpra/vcZjKZmD59+qDPNRh79+7l17/+Na+99hp79+4dcF+fz8fkyZO1nzdt2qS1a86cOf0eZzQamTt3Lu+//36vbZFIhO3btwMwf/78Aa8/0N9FjF5D8m54ww038OSTT2pd38cffzxXX301l1xyCRMmTBj0eaLRKB988AFPPfUUL730Ej6fjx/96Ec89NBDPPHEE5x88slD0Xwxxnk8HrxeLxaLBb/f36s6DqClDWlpadGG6lK9TYqi4PP5Djo5XYxO/U2LOBSjfVpEbm5uv9tS1aYOVX8jTAebUtAzJ+xAwflzzz2nff/www8fNGg5nPsIh8OD3vfll1/ut7evtLR0wBX0h+qtt97i0ksvHfQo3oH30draCnQH7wf7/zpu3Lg+H29ra9OeLwfOyR3sOcToNiQB5eOPP47JZOL666/nBz/4wWF/EjOZTJx55pmceeaZPPLIIyxbtoyf//zn1NTU8Pe//10CSnFYnE4ndrud3NxcGhsb8Xq9fS680Ol0uFwuzGazNtSdSCTIyspCp9Oxe/duVFWltLR0VKaGEX3rOS0iFAqlBYeDoaoq4XBY+6AyGkdTBgoqegZ169atG3DuXk9DXd7x7LPP5qOPPiIYDHLLLbdQXl4+4Ie+1H185Stf4Ve/+tWQtm0o+Xw+rrrqKkKhEFlZWdx+++2cffbZTJkyhZycHEwmEwB///vf+dKXvgTQ7welo/U6Jq+Hx6YhCSi/853v8KMf/Yjx48cftXOazWauueYarr76apYtWzbsw0hi9FIUhbKyMqqqqnC73Xi9XhoaGsjJyemzt1Kv16MoColEArvdjtFo1BKj79mzB7/fz3HHHae9cIvR7WDTIg4m9cHD4XCMyWkRPYenU4neR4KFCxfyb//2b5x77rk0NzfzpS99ieXLlzNjxow+98/Pz9dW8w9FacolS5b0uRjoaHv++ee1uaUvvfQSZ555Zp/7pXoh+5LqkW5paSGRSAz4gaKpqanPx3tORehvn8FuF6PTkCzK+d3vfndUg8meFEXh8ssv52tf+9qQnF8cGxwOB+Xl5TgcDgoLC9HpdHi9Xmpra/H5fLS0tODz+aitrcXr9WI2m5k2bRpOp5OCgoK0Xqe2tjbWrFkjFSDGEI/Hg8lk0qZFDHboO5lM4vf7sVgsmEwmiouLh7ilmTd37lzt+48//ngYW9LbqaeeymuvvYbVaqWxsZHTTz9dm9d3oNR9rFmzhmg0mslmHlXV1dVA93B1f8EkpM99PVBFRQXQPc1soLRA8XicqqqqPrdZLBamTZsGcNCqOodadUeMDlJ6URyz8vLyqKysJC8vj8LCQjweDzabjVgsRiQSIRaLYbPZ8Hg8FBYW4nK5OPvss/tMbxKNRqmqqmLPnj0jvkybOLie0yJisRher/egf1dVVfH5fMRiMXJzc7Hb7eTk5GSoxZlz5plnavMe/+d//mfE/X8/44wzeOWVV7BYLDQ0NHD66aezc+fOXvtdcMEFQHc1mcceeyzTzTxqUmmpurq6tHULBwqFQn3mnkzpGYg+/vjj/e730ksvDVhOM3WeTZs2sX79+n73+/Of/9zvNjF6SUApjmkOh4N58+ZRWVlJcXExLpeLoqIiPB4PRUVFuFwuiouLqaysZN68eWRnZzNhwgQqKyv7HOLes2cPGzZsGNU9HuKf0yJsNhtut5twOExDQwPBYLBXAKWqKsFgkIaGBsLhMG63G5vNRllZ2ZicS+Z0OrnlllsAWLlyJbfddlu/gQx0D2/+8Y9/zFTzADjrrLN4+eWXMZvN1NXVcfrpp7Nr1660fa6//nptJO3222/ngw8+GPCcH330EStWrBiyNh+uVK9gKBRKW5iUkkgk+OY3v0l9fX2/51iwYAHHH3880L2gqa+cnw0NDdx+++0DtuVb3/qW9n/+pptu6jP36FNPPcWbb7454HnE6DS2JvcIcRgURcHpdOJ0OonH40QiEW0ekdls7nMOnNPpZN68edTU1PSam9Te3s7q1auZOXPmgKtlxciWmhZRXV2NXq+nra1Nq9Odyk+ZWoCTSCSwWCwUFhZis9m06RRj1U9/+lNWrFjBZ599xn//93+zfPlybrzxRiorK7Hb7bS1tVFdXc27777LW2+9RUVFBd/85jcz2sazzz6bF198kYsuuoj9+/dzxhlnsGLFCq0EpNls5rnnnuO0006js7OTM844gyuvvJILL7yQSZMmkUwmaWhoYO3atbz00ktaFZhTTz11SNvd2Ng4qATqM2fOZMGCBVx++eXceeedRCIRbrjhBqqqqjjrrLPIycmhurqa3/72t6xdu5ZFixYNOEXhoYce4uSTTyYWi3HWWWf1WXrR5/MxZ86cfofF58yZw3e/+11+97vfsWbNGubNm8ePfvQjKioqtNKLjz76KPPmzRtwCF6MTsMSUMbjcd544w0+/PBDdu3aRSAQOOgiG0VReO+99zLUQnGsMhgMg15EYTKZqKioYP/+/dqK75RYLMaGDRsoLS1l4sSJY7Kn6liQmhZRU1OD2WwmGo1q5ThTq79tNhsOhwOTyYTdbqesrGxMB5PQHYz97W9/Y8mSJbz44ots2LBB67XsS3Z2dgZb90/nnnsuzz//PJdccgl79+7l9NNPZ8WKFVrP5MKFC1m+fDmXX345+/fv56mnnuq3RCRk5j62bt06YHLxlO9973ssWLCAkpISHn74Yb75zW/S1dXFL3/5S375y1+m7XvFFVdw4403DjjH8sQTT+SJJ55gyZIldHV18Ytf/IJf/OIX2naDwcBDDz3Exx9/POA8ywceeID6+npefPFFampqet3LpEmTePbZZ5kyZcpB71GMLhkPKFesWMGSJUvYt2+f9thAc3BSvQBj8Q25ubmZVatWsWrVKlavXs3q1atpaWkBuodjDrXM11tvvcWjjz7K6tWrtVQ48+fP56abbpJSV0NEURQmTJhATk4OW7Zs6VUVZe/evbS3tzNz5sw+08cMtkdUDJ/UtAi/309dXR0+ny/tNUtRFG1qRE5Ozph8reqLw+HghRde4KOPPuLxxx/nww8/pL6+nnA4THZ2NlOmTGHBggWcd955/ZZxzITFixfz3HPPcfnll7N7925OP/10li9frq1OX7hwIdu3b+cvf/kLr732GuvXr8fn86HT6XC73Rx33HGceuqpXHLJJf2uGB9uN9xwAzNmzOD+++/n448/pr29HZfLxZw5c7jhhhu4/PLLWb58+UHP87WvfY05c+Zw33338d577+Hz+XC73SxatIj/9//+HyeeeOJBF2IZjUZeeOEFli5dyqOPPqoVKCktLeWiiy7i9ttvl5GbMUpRMzijuqqqii984Qvap/vUqjCn05mWvLY/fWXnH80GeuM5lIAymUxy00038ac//anffb75zW/yhz/8YVC/50NVW1urfeLfv3//iEkjkmmxWIyamhrtQ0FPRqORsrIy8vPzUVWV9vZ26uvr+w1OPB4PTqfzmAlORhP5ECCEGM2G6j07o6+Cd999N5FIBLPZzAMPPMANN9yAxWLJZBNGrAkTJlBWVsY777xzyMf++Mc/1oLJuXPncscddzBlyhR27tzJr371K9avX88f//hH3G43P//5z49208U/GI1GZs2aRW1tLbt27eo1BL5p0yZcLhehUIhQKJQ2fJpMJtHpdJhMJiKRCF6v95gZPh1tDmVahBBCHCsy2kPpcrloa2vjnnvu4d///d8zddkR66677mL+/PnMnz+fcePGsWfPHiZNmgQMvody27ZtlJeXE4/HmTdvHh988AFWq1XbHgqFOPXUU1mzZg0Gg4HPP/+cqVOnHtX7kB7K3jo6OtiyZQtdXV3aY+FwmObmZnQ6HTqdjlgsNuACj9zcXG2BR896xkIIIcThGqr37IymDUq9uZ5zzjmZvOyIdc8993D++ecfUV3TBx98UMtD9tvf/jYtmITuOrm//e1vge6huv/6r/86/AaLQcvOzuaEE07QKupEIhGam5uJRqO0tLTQ1taGw+GgpKQEl8tFfn4+LpeLkpIS3G43yWSSxsZGAoEA1dXVBAKBYb4jIYQQon8ZDSgnTpwIdA//iSOnqiqvvPIKAGVlZSxcuLDP/RYuXKhNJn/llVdGXCLiscpoNFJeXs6UKVNoaWkhFosRDAYxGo3Y7XYCgQBtbW295lDa7XaKioqwWq14vV5CoRA1NTXydxNCCDFiZTSgvPDCCwEOmkBWDM7u3bu1ZLUHy42W2l5XV8eePXuGumniHxRFISsri/z8fG2Iu2e98I6ODhobG3t9yFIUBbfbjdFopK2tjWAwiN/vH45bEEIIIQ4qowHl9773PYqKivj1r38tQc1RsGXLFu37srKyAfftuf3zzz8fsjaJ3urr61EUBYvF0ufK7UgkolVh6UlRFHJycujq6iIajVJXV5fJZgshhBCDltGA0u128+abb2K1WjnxxBP53//9X+l1OQK1tbXa9webVJuagAvdk3AP9ToDfTU0NBxaw48h8Xgcn89HIBDAYDBQXFxMfn5+r6AymUzi9XppaWlJG9q22Wzo9XoCgQA+n0+bLyuEEEKMJBnPfTF79mw++OADTjzxRL797W9z880343K5sNlsAx6nKAo7d+7MUCtHh54LNbKysgbc1263a993dnYe0nV6BqPi0EQiEVRVJRqNYrVa0el0OBwOzGYzXq+311B3IBAgEolow92KomC1WrXcrZFIRFLWCCGEGHEy/s70wgsv8I1vfINAIICqqqiqSnNz80GPkwTPvfVMSWMymQbct2eVlnA4PGRtEulSJUWTyWTa/2GTyURRUREtLS29hrqj0Sj19fW4XC7sdruWUqjn+YQQQoiRJKMB5SeffMKVV16pvSmWlpYye/bsQVfKEel6JoWPRqMD7tuzJOCBqYUO5mBD5A0NDSxYsOCQznms0Ov1AOh0ul6rtHU6HS6XC4vFQmtra9p2VVXxer2Ew+G00qOp8wkhhBAjSUYDyp/97GckEglycnJ46qmnOPfcczN5+TGnZwWVgw1j9+wFO9jw+IEkUfnhM5vNKIqCyWQiFAr1qkuvKMpBh8BDoRAulwtFUfqsBy6EEEIMt4x2C65ZswZFUbjnnnskmDwKegZ6PRfo9KVnL6PMicwcg8GAy+XC4XCQSCQIhUJ97pcaAj8w2I/FYlr+Sin5J4QQYqTKaECZejM9+eSTM3nZMWvmzJna9zU1NQPu23P7cccdN2RtEr15PB5MJhMWiwW/399vgvLUEHiqN7LnIhydTkcgEKCmpkbmUQohhBhxMhpQpupU99dLIw7NpEmT8Hg8AKxYsWLAfVPJ5IuLi7WKRSIznE4ndrud3NxcYrEYXq93wKo3WVlZFBUVEY1GtbreRqMRs9lMY2Mja9eu7bWQRwghhBhOGQ0oL774YlRV5e23387kZccsRVH46le/CnT3QH766ad97vfpp59qPZRf/epXZcV8himKQllZGTabDbfbTTgc1hKZHxhYqqpKMBjE5/NhNpspKCjAaDRqvZbQ/YFs7dq1NDQ0SDlGIYQQI0JGA8of/OAHTJs2jQcffJA1a9Zk8tJj1ve//31t5e+tt97aKyVQOBzm1ltvBbrn833/+9/PdBMF3QuoysvLcTgcFBYWotPp8Hq91NbW4vP5aGlpwefzUVtbi9frRafTUVRUxPjx4znppJN65WlNJpNs3bqVzz//XJKdCyGEGHYZneHvcDh47733uOyyyzjllFO47bbbuOKKK5g+fXpaCpxjxUcffcSOHTu0n30+n/b9jh07+Mtf/pK2/5IlS3qdY/r06fzwhz/kvvvuY82aNSxatIgf/ehHTJkyhZ07d/LLX/6S9evXA/DDH/6QadOmDcm9iIPLy8ujsrKSmpoazGYz0WiUQCCgJS1XFAWbzYbD4cBkMmG32ykrK8PhcFBcXMyWLVt6reZvbm6mo6OD4447jpycnGG6MyGEEMc6Rc3gmFnPHHoHpk85GEVRxlxPzJIlS3j88ccHvX9/f6pkMsmNN97In//8536P/cY3vsGjjz46JPk+a2trtZXj+/fvlzRDB6GqKn6/n7q6Onw+X9rfVVEUXC4XxcXF5OTkpD1HkskkO3fu7LOmt6IoTJw4kQkTJsiUBiGEEP0aqvfsjA55pyrjpN5Ae/48mC/RN51Ox5/+9CfeeOMNvvrVr2qrij0eD1/96ld58803+eMf/yjJ40cIRVFwOp2Ul5ezaNEi5s+fz/HHH8/8+fNZtGgR5eXlOJ3OXoGhTqdj2rRplJeX90ofpKoqu3fvZsOGDWkVlPoSj8cJBoN0dHQQDAbH3Ac1IYbKnj17UBQFRVF6jSAB3H333dr2I3HaaaehKAqnnXbaEZ3nWPWXv/xF+zvs2bOn1/YlS5ZoH8LF0ZPRIe+77rork5cb8f7yl7/0+aJ0uM4991zJ7znKHE5uSbfbjcPh4PPPP8fv96dta29vZ82aNUyfPp2CggLtcVVVaW9vp76+vt9eUY/H02cgK4QQQhyMBJRCjEIWi4XKykr27t3b6xN4PB5ny5YttLS0MG3aNMLhMDU1NQSDwbR5m8lkEp1Oh8lkIhKJ4PV60+ZtCiGEEIMlZTeEGKVSQza5ubl8/vnnvYa6m5qaaGhoIJFIkEwmaWtro6urC71ej9Vq1ZKnh0IhAoEAFouF3NxcqqqqKC8vJy8vb5juTIiRaeLEiTL9agw42qODopsElEKMcjk5OcybN4/t27fT1NSkPR6JRGhsbNQSpDscDtxuNzabLW1YOxVU+v1+GhsbcbvdVFdXU1lZKT2VQgghBmXYA8qmpiY2b95Ma2sr0J1aZdasWYwbN26YWybE6GEwGDjuuOPIy8tj+/btxGIxfD6fVgfcaDSiKAomk6nXHElFUbDb7dhsNrxeL16vF71eT01NDfPmzZM5lUIIIQ5qWJb9qqrKH/7wByoqKvB4PHz5y1/myiuv5Morr+TLX/4yHo+HiooKHn30URleEOIQjBs3jnnz5mEymYjFYtoQt81mIxqNUl9fT0dHR5/PK0VRcLvdGI1G2traCAaDvRb9iJHvwJXGfr+fe++9l7lz52qLrvoa7nv55Ze57LLLmDBhAhaLBafTybx587jnnntoa2vrtf/evXvR6XQoisKPf/zjg7brmWee0dr15ptv9rnPjh07uO2226ioqCAnJwer1crkyZNZsmTJgMUwli9frp17+fLlJJNJ/vznP3P66aczbtw4dDpdrzy+a9eu5Rvf+AbTp0/HbrdjsVgYP348J5xwAt/97nd59dVXez1PDrbK+0Dt7e3cddddlJeXk5WVRV5eHqeffjrPPPPMQY8djMbGRn784x8zb9488vLyMJvNjB8/nssvv5x33333iM/f1dXF//zP/3Daaadprw15eXnMmDGDr3zlKzzwwAN9rqJOSSaTPPPMM1xyySVMmDABq9WK1Wpl+vTpXH311Tz//PPEYrFex23evJmf/exnnH322ZSUlGA2m8nKymLatGlcf/31/VaFG6yDrfJO/Y3vvvtuAFavXs3XvvY1rS3FxcVce+21fP755we9VigU4t5772X27NnY7Xby8/M5+eST+fOf/4yqqr3+745qaoa1traqJ598sqrT6VSdTqcqitLnV2r7ySefrLa1tWW6meIQ7N+/XwVUQN2/f/9wN0eoqrpp0yZ12bJl6n/913+pf/jDH9THHnss7euZZ55R//a3v6nvv/9+r6/XX39dfeyxx9S3335b3bx583DfijhEd911l/Z83LZtmzpx4kTt59TXY489pu3f2tqqnnHGGb326flVUFCgfvLJJ72udfLJJ6uAOmnSpIO267zzzlMB1e12q7FYrNf2+++/XzUajf22QVEU9Sc/+Umf537//fe1/d566y31zDPP7HX89ddfr+3/wAMPqDqdbsB7BtRAIJB2nd27d/f5O+zrd79r1y51ypQp/Z778ssv7/P3oKqqeuqpp6qAeuqpp/b7+1y6dKlqt9sHbP83vvGNfq9xMPX19erMmTMP+jv6wQ9+0Ofxu3fvVisrKw96/Pvvv592XM+/5UBf//qv/9pv2x977DFtv927d/fafv3116uAWlpa2ufxqWPvuusu9fe//71qMBj6bIPNZlNXrFjRbzv279+vTps2rd97OP/889V33nmn39/FUBmq9+yMDnmrqspXv/pVPv74YwDy8/O5/PLLOfHEEyksLAS6P3GtWrWK5557Dp/Px8qVK/nqV7/KihUrMtlUIUateDxOS0sLiqKQk5OD0WjslWuyq6uL+vp68vLysNvtacPaNpsNvV5PIBDA5/MRj8cPObWRGBkuvfRS6urquPXWW7ngggvIzc1l+/btlJaWAt3zbM8880zWrVuHXq/nqquu4txzz2XSpEnEYjE++OADHnjgAZqbmzn33HNZv369dizA1VdfzUcffcTu3btZuXIlJ510Up/taGlp4Z133gHg8ssv7/X/6f777+eOO+4AYPbs2dx8881MmzYNp9PJ1q1b+d3vfscnn3zCvffei8vl4l/+5V/6vecf/ehHbNy4kQsuuIAlS5ZQWlpKU1MTHR0dAGzcuJHbb7+dZDLJpEmTuOWWW6isrCQvL49AIMDWrVt5//33eeWVVw7/Fw9cccUV7N69m29/+9tceuml5OTksHHjRn75y1+ybds2nnvuOTweD//1X/91yOd+7rnnuPbaa1FVlcmTJ3PLLbcwc+ZM3G43e/bs4U9/+hNvvvkmf/rTn8jOzuaBBx445GvceuutbNmyBYBrrrmGiy++GI/Hg16vp6GhgTVr1vT7O2pqamLRokXU19cDcMYZZ3D99ddTVlaGoijs3r2bv//97yxbtqzXsfF4HLvdznnnnccZZ5xBWVkZ2dnZNDc3U11dzf/8z/+wd+9e7rvvPqZPn84NN9xwyPc2WG+//TarVq2ioqKC733ve1RUVBAOh3nppZf47//+b0KhENdeey3bt2/HZDKlHRuLxTjvvPPYvn07AOeddx433ngjJSUl1NbW8uijj/L666/j9XqHrP0Zd9RC00FYunSp1vt4zTXXqB0dHf3uGwgE1Ouuu07b/+mnn85gS8WhkB7KkaWzs1N9//331aefflpdtmyZ+t5776kvvPBCr17K1Nezzz6rvvvuu2m9lMuWLVOffvpp9f3331c7OzuH+5aOumQyqUYikWH9SiaTQ3JvPXvJdDqd+vbbb/e775133qkCqtPpVNesWdPnPnv27FGLiopUQL3qqqvStvl8Pq1X8bvf/W6/13n44Ye1Nq1cuTJtW3V1tXaOu+66q8/fSyKRUK+55hoVULOystTW1ta07Qf2av37v/97v235yU9+ogKq3W5XGxsb+92vvb1dTSQSaY8dSg8l0Of7VkdHhzpnzhzt77Np06Ze+wzUQ+n1etWcnBwVUL/+9a/32wOZ+tvqdDq1pqam3/vsSzgc1v4m/fVAprS0tPR67KKLLtJ+B7/85S/7PTYQCPT6W3q93gFHJSORiHrWWWdpPYzxeLzXPkerhxJQzz33XDUSifTa52c/+5m2z4svvthr+4MPPqht//73v9/ndW655ZYBe2uHypjooXz66acBOPXUU3nyyScH3DcrK4vHH3+cffv2sWLFCpYuXcrXvva1TDRTiFEtkUgA3fOXFEVBp9ORl5eH1WqlpaWlV29lKBQiEomQn5+PzWYD0FIK9TzfWBKLxVi5cuWwtuGkk07q1atxtC1ZsoQvf/nLfW7r7Ozk97//PQD33nsvJ5xwQp/7lZaW8pOf/ITvfOc7LFu2jEcffRS73Q50jzKdc845vPbaazz33HM8+OCDffZmP/XUUwBMnjyZL3zhC2nbfvOb3xCLxZg3bx533XVXn4vAdDodv/3tb1m2bBmdnZ08//zz3HjjjX22d/r06drct740NjZq+w20+DMnJ6ffbYNx/vnn9/me5XA4ePTRRznxxBNJJpM88sgj/O53vxv0eR9++GH8fj/FxcU89NBD/Y4e3HPPPTz++OPU1dXxxBNP8J//+Z+DvkZra6s2t/GUU04ZcN8D04tt3bqVl19+GYALL7xQ63nuS1ZWVq/HXC7XgNczmUzcf//9Wh7eqqqqfv/vHimLxcJjjz3W5/P0X/7lX/jpT39KNBrlww8/5KKLLkrb/sgjjwBQUlLCfffd1+f5f/WrX/Hiiy9qPbmjXUYX5axbtw5FUbjlllsGfcytt94KwPr164eqWUKMKXq9Huh+E1Z7LCqwWq14PJ4+X8QTiQTNzc34fD6SySSqqmpv7KnzidHn6quv7nfbihUrtEVXl1566YDnSQUVsViMtWvX9nkNr9fL3/72t17H7tu3T5vmdNVVV/Xa/tprrwFwySWXDJhRwOl0UlFRAcAnn3zS735XXHHFgP9ni4qKANiyZQurVq3qd78jNdBQ7IIFCygvLwc45MUzr776KtAdsJrN5n73MxgMWvA+0O+rL/n5+VoQ9eSTTx5SedY33nhDe9257bbbDum6fYlEIuzbt48tW7awefNmNm/enPa6tmHDhiO+Rn/OOuustIpjPTkcDqZNmwbArl270rbV1dVRU1MDwGWXXdbv38lqtXLZZZcdxRYPr4wGlKnUQJMmTRr0Mal9U8cKIQZmNpu1FEHhcDjtxVen0+FyuXC73X3Wdu/s7KSuro6Ojg4txdBAb1piZJs9e3a/23qumi4qKtJWmvb1NWvWLG3fVA9fygUXXKDlK031RPb0zDPPaP8HDwxw9+7dq80h+7d/+7cB26AoitbmA9sw2HsG+NrXvobRaCQSibBo0SIWL17MI4880itQOVLz588fcPuCBQsA2LZtG9FodFDnTCQSVFVVAfCHP/zhoL+v559/Hhj499UXs9nMFVdcAcDzzz/P1KlTueOOO3jzzTdpb28f8NhU54/RaGThwoWHdN2UYDDIL37xC+bMmYPdbqe0tJTy8nIqKiqoqKhg7ty52r4+n++wrjEYZWVlA25P9c4GAoG0xzdv3qx9f7De03nz5h1m60aejAaUqSGEQ+nebWhoACA7O3tI2iTEWGMwGHC5XDgcDhKJBKFQqNc+drud4uJibYi7p3A4TEdHB4lEgry8PFmQM4rl5ub2u625ufmwznng/yer1aoN97388su9tqeCzOOPP77XG/TRakNPA90zdAcJzzzzDLm5ucTjcV5//XVuvvlmKioqKCgo4Nprr+XDDz88rHb11F/PVkpquF1V1T7TMvWltbX1kHoLUwb6ffXnd7/7HYsXLwa6A//777+f8847j/z8fObPn8/999/fZ1qxVICXl5d3WFM69uzZQ0VFBXfeeScbN2486JSbcDh8yNcYrL5eH3tKfSg/sI09/55ut3vAcxxs+2iS0XeKWbNmsWLFCh577DHOO++8QR3z2GOPaccKIQbH4/Hg9XqxWCz4/f5e1XGgeyjb7XbT2dlJa2srqqqiqiqRSASDwUAoFKK1tZXOzs4+h8lHM6PR2O+K5Ey2YagNNPTb801w3bp1g25PSUlJr8euvvpqnnjiCYLBIK+88oo2d7C6uppNmzZp+wzUhv/4j/8Y9PBfag5nXwYzReOSSy7hzDPP5Nlnn+Xtt9/mww8/xOv14vP5WLp0KUuXLuX666/nz3/+c589+YMxFAUBev6+vvnNb/K9731vUMcdTmCXnZ3Nq6++qmVdWb58OVVVVSQSCdasWcOaNWv49a9/zcsvv9xrXuyRuPbaa9m9ezeKonDDDTdw5ZVXctxxx+F2u7VRk2Qyqf2dj2avsjgyGQ0oL730UpYvX85LL73E3Xff3e8E7JR7772XF154AUVRxtQ8AyGGmtPpxG63k5ubS2NjI16vF7fb3WeVHIfDgcViwefz0draSiKRICsrC6PRSDKZZO3atUycOJEJEyaMmao5qSkBx7L8/Hzte7fb3WegOFhf+tKXGDduHE1NTTz11FNaQJnqndTpdFx55ZUDtsFoNGa04yAnJ4ebbrqJm266CYDPP/+cV155hd/+9rfU19fz+OOPM3fu3EEHbQdqampi/PjxA26H7v+LB+tVTem5AEZV1Yz8vhYsWKANzwcCAZYvX85f/vIXXnzxRZqbm7nkkkvYuXMnVqsV+OeimtbWVqLR6CE9z2pqavjoo48AuPPOO/nZz37W534jfQpcz7/nwdICjaW0QRkd8r7xxhuZMWMGqqpy7733MmfOHB544AE+/vhjtm/fzo4dO/j444954IEHmDNnjrZSr6ysrN8VfUKI3hRFoaysDJvNhtvtJhwO09DQQDAY7PWJXlVVotEoyWQSg8GgBZMul0tb7b17927Wr19/WENnYmTqOQ8ttWjmcOn1ei1gfOedd2hpaUFVVa0izOmnn47H4+l13OTJk7WpUEfahiN13HHH8a//+q98+umnWg/oc889d9jnW7169aC2T5s2bdBBl8lk0hbzDMfvy+FwsHjxYl544QUtF2hDQ4MWBEL31AboXsB1qIuBqqurte9Tczj7MlDVpJEg9TcCei1iO9BIv5dDkdGA0mg08tZbbzFp0iRUVaW6upof/vCHnHLKKZSVlTFjxgxOOeUUfvjDH2oTpCdPnsxbb70l87iEOEQOh4Py8nIcDgeFhYXodDq8Xi+1tbX4fD5aWlrw+XzU1tZq9bsnTpzIpEmTmDhxYq/FOB0dHaxZs4a6ujoZZhoDzjzzTG2O2P/8z/8c8d80NaQdi8V47rnnWLlypVaWr7/V5nq9nnPPPRfoDkQHU8puqI0fP57p06cDR7bg4/HHH+932+rVq7WFG2eeeeYhnfeCCy4Aunvz3n777cNu35H60pe+pH3f8/d03nnnaSMZDz744CGds+f80GAw2O9+qZQ8I1VJSYn2f2jZsmVEIpE+9+vq6uozuftolfFa3hMnTmTjxo384Ac/ICcnR5u3deBXTk4Ot99+O1VVVUyYMCHTzRRiTMjLy9OqgBQWFuLxeLDZbMRiMSKRCLFYDJvNhsfjobCwkLy8PBYsWMAXv/jFtIooKclkku3bt7Np06Z+XyTF6OB0OrUUbitXruS2224jmUz2u39TUxN//OMf+90+f/58LY3KU089peUdtlgsXHLJJf0e92//9m/o9XqSySSXXnoptbW1/e6bSCR46qmnBtznYF5++eUBVyrv379fS/lyKBlJDvTqq6/22cPZ2dnJt771LaB7KkDq+8H63ve+p81pvuGGG9J69fryxhtvsHHjxkO6xq5duw5anS5V+QjSf0/Tp09PW6R1//3393uOYDCYtoAl9f8H6LdW+sMPP3zEVYwyIfV3ra2t5V//9V/73OeHP/zhmMlBCRmeQ5lit9u5//77+c///E/Wrl3L5s2btTkReXl5zJo1ixNOOOGYn+MkxNHgcDiYN28efr+furo6fD5fWm+Uoii4XC6Ki4vJycnRehcmTZpEXl4eNTU1vVZStra2snr1aqZPn37Q1axi5PrpT3/KihUr+Oyzz/jv//5vli9fzo033khlZSV2u522tjaqq6t59913eeutt6ioqOCb3/xmv+e7+uqrufvuu1m5cqXWA3f++ecPmKWjoqKCX//619x2221s2bKFWbNmcdNNN3HGGWcwbtw4urq62LNnD5988gnPP/88DQ0NbNq06bDnfD744INcffXVWmm/4447jpycHNra2lizZg2//e1vtf/v3/72tw/rGtCdDuaqq65ixYoVXHrppWRnZ2ulF7du3QrAd7/73YOmOTrQuHHjePzxx7n00ktpaGhg3rx5LFmyhK985SuUlJQQi8Wora1l1apVPP/88+zatYvXXnvtkK6zb98+Tj/9dGbOnMlFF13EvHnzKC4uBroD7meffVYLlisrKznxxBPTjn/ooYf49NNPqa+v54477uD//u//WLJkiVZ6cc+ePbz//vv89a9/5YUXXuC0004DuqdhzJo1i82bN/OHP/yBtrY2rr32WoqKiqitrWXp0qU8//zzLFq0aNinSBzMLbfcwmOPPcbmzZt58MEH2bFjR6/Si2+88QYLFizQ8qGO+jnqR63mziCsWLFCXbFihRoKhQZ9TDgc1o4TI5OUXhxdYrGY2tnZqfr9frWzs7Pf0m0p8Xhc3bp1a1ppxp5f1dXVajQaPWrXE0emZ/m/wejo6FAvvvjitBJw/X2dfvrpA55r+/btvY556aWXBtWORx99VLXZbAdtg8lkUrdv3552bM/SiwcrX5cqazjQl06nU++9995exx5K6cVdu3apkyZN6vcal1xySb/PhYFKL6a8+uqral5e3qDu5e9///uAv5MDHVjKsr+vsrIyddeuXX2eY+fOneqsWbMOeo4D/17r169Xc3Nz+92/oqJCra+v136+6667el37aJVe7OvcPR3s77R37151ypQp/d7Ll7/8ZfWtt97Sfv70008HvN7RMiZKL5522mnodDo2btzIzJkzB3VMXV2ddtzh5N8SQqQzGAyHNCdZr9czffp0XC4XNTU1vZIwNzc3097eTllZmbYKVVVV2tvbqa+v77dH1OPx4HQ6R/+n8lHO4XDwwgsv8NFHH/H444/z4YcfUl9fTzgcJjs7mylTprBgwQLOO++8fss4pkydOjWtxyU3N1ebI3kwN954IxdccAF/+MMfeOedd9i6dSvt7e2YzWaKi4upqKjgrLPO4pJLLjloeb6BPPPMM7z++ussX76cLVu20NjYiM/nw2KxUFpayimnnMK3v/3tQ+45PNCkSZNYu3Ytv/71r3nppZfYu3cvRqOROXPmcNNNNw1YxWgwFi9ezO7du/nf//1f3nzzTaqrq2ltbcVgMFBYWEh5eTlnnHEGl1566YCrzfvyxS9+keXLl/P222/z6aefsn//fpqamujq6iIvL485c+Zw8cUXs2TJkn4LH0yePJmqqiqWLl3KsmXLWLduHT6fD4PBwPjx41m4cCGXXHIJX/ziF9OOq6yspKqqil/84he89dZb1NfX43A4mDp1Kpdffjnf/e53sVgsh/17y6QJEyawYcMGfvOb37Bs2TJ27tyJ2WymrKyM6667jm9961ta5SM48nKfw01R1czNrtfpdCiKwqZNmwYdUO7cuZNp06ahKMqYrCk8FtTW1movWPv37z+i9CNiZIvFYmzfvr3fhNQej4eCggK2b99OMBgkGo0SCAS0VeQ6nQ6TyYTD4cBkMmG32ykrK9MqrQghxLHkZz/7GT/5yU8wGAwEAoGMBMtD9Z494pdOpyaJSz1hIYaf0Whk5syZuFwutm3b1mvUYOfOnaxZs4asrCxCoRBdXV3o9XqsVquWgigUCmkvnLm5uVRVVVFeXp6WY08IIcY6VVV59tlnge6e2dHS89qfER9Q7t27Fxj9XcFCjCUFBQXk5OSwdetWbUFdJBKhubmZaDSK1+vFbrdTWFiI3W5PG9ZOBZV+v5/GxkbcbjfV1dVUVlZKT6UQYszYs2cPJSUl/U4x+o//+A9t8dr111+fyaYNiSENKPft29fn4w0NDQct5RaJRNi5cyc/+clPUBQlLVGoEGL4mc1mKioqaGhoYMeOHfh8PmKxGMFgEKPRiF6vx+/3YzQa0+ZZKYqC3W7HZrPh9Xq1HJg1NTXMmzdP5lQKIcaEv/zlLzz22GNcddVVLFq0CI/HQywW4/PPP+fxxx9n+fLlAMycOXNMFG8Z0oCyrxxeqqoedGJ3X6677rqj0SQhxFGkKAoejwdFUairq9OGuFO1w2OxGA0NDWRnZ+N0OtPqIiuKgtvtpqGhgba2NsxmM36/H6fTOXw3JIQQR9G+ffu47777+t1eVlbGG2+80e/iptFkSAPK/tb7HMo6IIvFwr/8y7/w9a9//Wg1SwhxlLW2tpKXl4ff70ev1/fqZezo6CAcDpOfn582T0hRFHJycvB6vUSjUerq6iSgFEKMCd/4xjfIycnhnXfeYceOHXi9XkKhkLZS/qKLLuLrX//6mMm5PaQB5WOPPZb28w033ICiKNx7771aktS+KIqCxWKhqKiIuXPnHnR4XAgxfOLxOD6fj87OTux2OwUFBbS0tPRKLxSLxWhsbOzVW2mz2dDr9QQCAXw+H/F4XEqtCiFGvfHjx3Pbbbdx2223DXdTMmJIX7UPnGR6ww03AHDhhRcOOm2QEGJki0QiqKpKNBrFarViNpspKirC7/fj9/t7jUh0dHQQCoXIz8/XVn9brVai0SiqqhKJRCSgFEKIUSajr9rvv/8+cGT1UYUQI0sqP2wymdSGuhVFwel0YrPZaGlp6VX3Ox6P09TUhMPhIDc3V0sp1PN8QgghRo+MBpSnnnpqJi8nhMiAVI5YnU7XqzfSZDJRWFhIR0cH7e3tvbYHAgHC4TCKomhD4JJzVgghRh/dwXcRQoj+mc1mFEXBZDIRDod7BY2phTcej6fPlYyxWAyfz0dXVxfJZHJMrHYUQohjzbBNVFJVlaqqKjZs2IDP5+vzjehA//Ef/5Gh1gkhBstgMOByuYhEIgQCAUKhEHa7vdd+RqORwsJCAoEAbW1t2vM9FouhqirJZBK/3097e/sR1WoWQgiRecMSUD7++OPcc889WhWcwZKAUoiRyePx4PV6sVgs+P1+LQ/lgRRFITs7G6vVSktLC+FwWFuEkyrRuHnzZgoKCpg2bRpGo3EY7kYIIcShyviQ949//GO+/vWvs2fPHlRVHfAL6PWzEGLkcTqd2O12cnNzicVieL3eAZ+zRqORgoICdDodyWQSi8WSVlGnubmZVatW0dzcLM99IYQYBTIaUH722Wf84he/AOCss86iqqqKdevWAd09F4lEAq/Xy1tvvcUFF1yAqqqcfPLJNDQ0kEwmM9lUIcQhUBSFsrIybDYbbrebcDhMQ0MDwWCwV0CoqirBYJDGxkagO+tDTk4OLpcrrVczFouxZcsWqqure+W0FEIIMbJkdMj74YcfBqC0tJQ33ngDg8FAdXW1tl1RFPLz8zn77LM5++yzefjhh/nud7/LOeecw2effTZmsskLMRY5HA7Ky8uprq5Gr9fT1tam1elO5ZtUVZVwOEwikcBisVBYWIjNZmPmzJnEYjF27NhBPB5PO6/P56O9vZ2pU6cybtw4qfUthBAjUEZ7KFeuXImiKPzLv/zLoBIX33zzzVxyySVs3LiRhx56KAMtFEIciby8PCorK8nLy6OwsBCPx4PNZiMWixGJRIjFYthsNjweD4WFhdr++fn5FBYWMn/+/D4X5MTjcWpqati8eXOvnJZCCCGGX0YDyoaGBgDKy8v/2QDdP5sQi8V6HXPttdeiqirPPvvs0DdQCHHEHA4H8+bNo7KykuLiYlwuF0VFRXg8HoqKinC5XBQXF1NZWcm8efNwOBzasWazmfLycmbOnNnngpyWlhZWrVpFQ0NDv3Mr4/E4wWCQjo4OgsFgrx5PIYQQR19Gh7xTAWNBQYH2WM863V6vF4/Hk3ZMSUkJADt27MhAC4UQR0OqUo7T6SQejxOJREgkEuj1esxm84AjFIqiUFBQgNPpZMeOHTQ3N6dtTyQSbN26lebmZmbMmIHFYkFVVdrb26mvr8fn86UFm4qi4HK58Hg8OJ1OGTIXQoghkNGA0u12U19fT0dHh/bYuHHj0Ov1JJNJPv/8814BZapXMxAIZLKpQoijxGAwHFZtbpPJxMyZM3G73Wzfvr3Xwpy2tjZWr17NuHHjaG9vJxQKEY1GCQQCRKNRkskkOp0Ok8lEJBLB6/Vit9spKytL6xUVQghx5DI65J0a6q6pqdEeM5lM2uN9DWs/+eSTAL0CTSHEscHtdjN//nwKCwt7bevs7OSTTz5h69at1NXVUV9fTygUwmg0aqmIQqEQ9fX1NDY20traSlVVFa2trcNwJ0IIMXZlNKD84he/iKqqvP/++2mPX3HFFaiqyp///GfuuusuqqurWbVqFd/5znd47rnnUBSFr3zlK5lsqhBiBDEajZSVlVFRUaHlqoxEIjQ3NxONRmlpaaGtrQ2r1arN28zPz8flclFSUoLb7SaZTNLY2EggEKC6ulpGPYQQ4ihS1AxmDa6urqaiooKsrCxqa2vJzs4GIBQKMWvWLPbs2dNrfpOqquTl5VFVVaXNpxQjS21tLePHjwdg//798ncSQyoej7Nz507WrFlDOByms7MTo9GoVecxm83k5+f3SjOmqiper5dwOKytMJ83b57MqRRCHFOG6j0740Pe77//Pi+99FLaykubzcb777/PokWLelXLmTVrFu+9954EKUIIoHtOZkFBAXl5eUSjUfR6fVqpx0gkQkNDA+3t7b0W57jdboxGI21tbQSDQfx+/3DdhhBCjCkZr+V96qmn9vl4aWkpH374IVu3bqW6upp4PM60adOYO3duhlsohBjp6uvrtYTpZrO514Kd1KrvUChEfn6+NkyuKAo5OTl4vV6i0Sh1dXU4nc5huAMhhBhbMh5QHsyMGTOYMWPGcDdDCDFCxeNxfD4fgUAAg8FAUVERkUiElpaWXrlso9EoDQ0NOBwOcnNz0el02Gw29Ho9gUAAn89HPB4/rFXoQggh/mnIXkU3bNjAnDlzhur0QohjVCQSQVVVotGoVtLRYrFQVFREe3t7WlqylEAgQCgUIi8vD5vNhtVqJRqNoqoqkUhEAsoMi8fjbN68Oe2xWbNmyd9BiFFsyJ69xx9/POPHj+e8885j8eLFnHHGGVKLWwhxxBKJBADJZDJtQY1OpyMvLw+73U5LS0uvYfBEIoHX602rK97zfCJzkskk7e3tvR4TQoxeQ7YoR1VV9u3bxyOPPMJ5551Hfn4+F110EX/+859pamoaqssKIcY4vV4PdAeQfSWpMJvNFBUVkZub2+cK7nA4TEtLC11dXaiqqp1PCCHE4RuygLK2tpZHHnmEc889F7PZTDAY5JVXXuHGG2+kuLiYE088kZ/97Gds2LBhqJoghBiDzGYziqJgMpkIh8N9BpWpxTcejwer1Zq2LTVc3tXVRUNDA5FIJFNNF0KIMSsjeSjD4TDvvvsur7/+Oq+//rpWTjHVe1BSUqINjX/pS1+SofFRRvJQikyrrq7WKuO43W7sdnu/+6qqSigUorW1lUQiQTQaJRQK4XA4cDgcFBQU4PF4mDx5sszhy5BEIkFjY2PaY4WFhdJbLEQGDNV7dkYTm6esXbuW1157jddff51169Z1N+QfwaXNZuPMM89k8eLFnHfeeYwbNy7TzROHSAJKkWltbW1s2LCBxsZGkskkRUVFB01QnkwmaW1tpaGhAUVRyMrKorCwEIvFAnSXgZ02bRoul0uSnQshxqwxFVD2VF9fz+uvv85rr73G3//+d8LhcHfDFAVFUTjhhBNYvHgxixcvllXjI5QElCLTVFVlzZo1tLa20tjYiNVqxe12DxgIpirlpNINGQwGPB5Pr2Py8/OZNm2aFmgKIcRYMmYDyp66urp49913td7LA4fGi4uLWbx4Md/+9repqKgYzqaKHiSgFMMhEAhQVVVFIBDA6/ViNBrJyclJq5oD/xzy9vv9xGIx3G43WVlZFBQU0Nzc3OfqYp1Ox8SJEykpKUGny2hBMSGEGFJD9Z49oiYMWSwWzj//fM4//3yge2g81Xu5bt06baHPuHHjJKAU4hjncDgoLy+nuroavV5PW1sbXq9Xq6CTSg0UDodJJBJYLBYKCwux2WyUl5eTl5dHaWkp27dvp7W1Ne3cyWSSXbt20dTUxIwZM8jOzh6muxRCiNFhRAWUBzrhhBM44YQTuOuuu6ivr+e1117jjTfewGazDXfThBAjQF5eHpWVldTU1GglGAOBgJa0XFEUbDYbDocDk8mE3W6nrKwMh8MBgNVqpaKiAq/Xy44dO3rlrgwGg6xbt+6gi3bi8TiRSIREIoFer8dsNssCHyHEMWXIhrxvu+02rr/+eiorK4fi9GIEkSFvMdxUVcXv91NXV4fP50tLJaQoCi6Xi+LiYnJycvqdZxmPx9m1axf19fV9bjeZTEydOlWbq5mqF15fX9/vNT0eD06nUxb5CCFGjFE3h1Kn06EoCuXl5Vx33XVcffXVFBUVDcWlxDCTgFKMJEfaW+j3+9m2bRvBYLDP7Xl5eRQWFrJ3716CwWBar2gymUSn02EymfrtFRXdf6MtW7akPTZz5kzp1RUiA0ZlQAn/XFCj0+k444wzuP7667nooot6JRsWo5cElGKsSSaT1NbWsmfPnl6LdsLhMF6vF4vFQiKRIBKJDDhvMzc3N23epoBoNMrKlSvTHjvppJMkB7EQGTBU79lDtnzx7bff5pprrsFms6GqKolEgnfffZdrr72WwsJCvv71r/P+++8P1eWFEOKw6XQ6JkyYwPz589OCwEgkQnNzM5FIhPr6elpbW3E4HJSUlOByucjPz8flclFSUoLb7SaZTNLY2EggEKC6uppAIDCMdyWEEENnyALKs846iyeeeIKmpiaeeOIJzjrrLO3TeyAQ4PHHH+fMM8+ktLSUH//4x9TU1AxVU4QQ4rCkFu2Ul5djMpnw+XzEYjGCwSBGoxGbzaalLYrFYtpxiqJgt9spKirCarXi9XoJhULU1NT0WSpSCCFGu4zmoWxoaOCpp55i6dKlbNy48Z+N+Mew+AknnMD111/PlVdeSX5+fqaaJY6QDHmLY4HX6+XDDz+kvr4eVVXJyspKW2yTqh9+4MIfVVVpaGhAp9NRWFhIZWUlTqdzGO5g5EgkEtTV1aU9VlxcLKUXhciAUTeH8mA2bdrEE088wTPPPKOtqky9CBuNRs455xyuu+46Fi9ejNFoHI4mikGSgFIcC1L1w/fv34/BYOi3p9FgMJCXl5eW3iwYDOL1evF4PBQXF1NeXp6pZgshRJpRN4fyYCoqKrj//vvZv38/77zzDtdee6023zIajfLaa69x2WWXUVRUxHe/+10+/fTT4WqqEOIYF4/H8fl8BAIBTCYT48ePx+Vy9dmjFo/HaW5uprm5mXg8DoDNZkOv1xMIBPD5fNrjQggxVgx7TTFFUTjzzDN5/PHH0+Zb6nQ6VFWltbWVhx9+mJNPPnm4myqEOEZFIhHtw67VakWn05GVlUVxcXG/6YBCoRB1dXW0t7cD3fMxUwnXI5FIBlsvhBBDb9gDyp5sNhvXXHMNb7/9NlVVVZSXl2vD4DKRXQgxXBKJBNCdTqjn/EidTkd+fj4ejwez2dzruJ7Jz1PBZM/zCSHEWDGissjGYjFee+01li5dyptvvpm2alIIIYZLamg7NXJyIJPJRGFhIcFgkLa2tl4BYywWIxQKYTAYiMfjsvhECDHmjIiA8uOPP+bJJ59k2bJl2vBQ6kXb4XBw6aWXcv311w9jC4UQxzKz2YyiKJhMJkKhkFYnvCdFUcjKysJqtdLe3p6Wc1JVVe0Dcn19Pc3NzZSWlmoFIIQQYrQbtoBy+/btPPnkkzz11FPs2bMH+GcQqdfrOfPMM7nuuuu46KKLsFgsw9VMIYTAYDDgcrmIRCIEAgFCoRB2u73PffV6Pfn5+WRlZdHa2kokEiEWi6GqKmazGYvFwt69e/F6vUybNo3c3NwM383wi8fjbN26Ne2xGTNmSOlFIUaxjD57fT4ff/3rX3nyySdZs2YNkD43sqKiQqv7XVhYmMmmCSHEgDwej1Zy0e/3Y7PZevVS9mQ2myksLCQQCLBv3z4MBgN6vZ7s7Gyge9HOhg0bcLvdTJ06tc85mGNVMpnE6/WmPTZt2rRhao0Q4mgY8oAyEonwyiuvsHTpUt5++20tXUYqkBw3bhxXXXUV1113HXPmzBnq5gghxGFxOp3Y7XZyc3NpbGzE6/XidrsHDCoBurq6sNvtWK1Wkslkr8DR6/XS2tpKaWkpJSUlMgwuhBiVhiygXL58OUuXLuWFF16go6MD+GcQabFYuOCCC7juuus4++yzZYK6EGLEUxSFsrIyqqqqcLvdeL1eGhoayMnJ6dVbqaoqoVAIv99PLBZj3LhxOBwOpk6dSn19fa+a3olEgl27dtHY2DioYfB4PE4kEiGRSKDX6zGbzTJcLIQYVkP2CnTGGWdotbuh+8X45JNP5rrrruPyyy/Xhn2EEGK0cDgclJeXU11djV6vp62tDa/Xi16vx2q1aq954XCYRCKBxWKhsLAQm81GeXk5eXl5FBYW0tDQwK5du3olOE8NgxcUFDBlypS03syeKYh8Pl/adCFFUXC5XHg8HpxO50F7TYebXq9n4sSJvR4TQoxeQ/qRVlVVpkyZwrXXXsu1117LpEmThvJyQggx5PLy8qisrKSmpgaz2Uw0GiUQCGh5JhVFwWaz4XA4MJlM2O12ysrKtAToiqLg8Xhwu93s2rWLhoaGXtdobm6mpaWFiRMnUlxcTDAYpKamhmAwmHa9ZDKJTqfDZDIRiUTwer29rjcS9RVQCiFGtyGr5f3tb3+b6667jpNOOmkoTi9GEKnlLY5Fqqri9/upq6vrt8ewuLiYnJycAXsMOzo62L59e69h8J7XiUajALS1tdHV1TVgj2hubm5aj6gQQvQ0VO/ZQxZQimOHBJTiWHekcxpVVe1zGDwSidDY2Eg0GiUWi+FwOMjLyzvonE23243D4aCysnJE91QKITJvqN6zh3UW986dO/nkk09obGwkFArxne98B5fLNZxNEkKIQ2YwGI5oUUxqGNzlcrF7924aGhpQVRWfz0csFiMYDGI0GkkkElo98Z4BpaIo2O12bDYbXq9Xm9dZU1PDvHnzRvycSiHE6DcsAeW6dev4/ve/z8cff5z2+KWXXpoWUP7+97/nnnvuIScnhy1btmA0GjPdVCGEyBiTycSMGTMoKipiw4YNxGIxbYjbZrMB4Pf7CQaD2tD2gYGl2+2moaGBtrY2zGYzfr8fp9M5THckhDhWZDzh2euvv86iRYv4+OOPUVVV++rLddddRzgcZteuXbz++usZbqkQQgyP7OxscnNzyc7OJpFIaKUfU+LxOF6vl6amJm1+ZYqiKOTk5NDV1UU0GqWuri7TzRdCHIMyGlA2NDTwta99jUgkwsyZM3nrrbf6nYgO3Sk6LrjgAgDeeuutTDVTCCGGVTwep6WlBVVVcTqd5Ofn97lfV1cX9fX1tLS0kEgktMdtNht6vZ5AIIDP5+uVnmi4xeNxampq0r5GWhuFEIcmo0Pe//Vf/0UwGKS0tJQPP/xwUMMwp512Gs888wxr164d+gYKIcQIEIlEtNXddrud/Px8HA4Hra2tdHV19do/EAgQDAZxOp04HA4URcFqtWqpjCKRyIhKfJ5MJmlsbEx7bPLkycPUGiHE0ZDRV5j/+7//Q1EUfvCDHwx6Tk9ZWRkAu3fvHsKWCSHEyJHqbUwmk9pQt8lkYty4cYRCIVpbW9N6JFP7tra2EggEtNfX1HSiA/cVQoijLaMB5d69ewFYsGDBoI9JVdTp7OwckjYJIcRIk6oao9PpeuW3TNUF9/v9dHR09JqDHovF8Hq9xGIxrdKOVKERQgy1jAaUqTkyyWRy0Mf4/X4AsrKyhqRNQggx0qQW4ZhMJkKhkFaBJ0Wn05Gbm4vD4aCtrY1gMJh2fCovZSq4HGk9lHq9XsuD1/MxIcToldFFOYWFhQDs2rVr0MesWrUKgAkTJgxJm4QQYqQxGAy4XC4cDgeJRIJQKNTvfm63m8LCwrS637FYDFVVMZvNqKrK+vXr2b59O7FYLFO3MCC9Xs+UKVPSviSgFGJ0y2hA+cUvfhFVVVm2bNmg9o9Go/zhD39AURROO+20oW2cEEKMIB6PB5PJhMViwe/395teDcBisVBYWIjb7Uav12uLcPR6PdnZ2aiqSl1dHZ9++il79+4dcT2WQojRL6MB5ZIlSwB49dVX+dvf/jbgvtFolOuuu46dO3eiKAo33nhjBloohBAjg9PpxG63k5ubqw1dDxRUKoqCzWbDZDJhMpmw2WwYjca0nstEIsHu3btZtWoVjY2NA55PCCEORUYDytNOO40rrrgCVVVZvHgxP/rRj7QhbYA9e/awcuVK7r//fsrLy1m2bBmKovDtb3+b8vLyTDZVCCGGlaIolJWVYbPZcLvdhMNhGhoaCAaDvQJBVVUJBoM0NDTQ1dXF+PHjmTx5MnPmzOlzKDkSiVBTU8PatWtpbW09aFvi8TjBYJCOjg6CwaDkjBRC9KKoGf6IGolEuOSSS3jzzTcHrC+batbFF1/Ms88+K/NrRrChKjQvhIDW1laqq6sJhUK0tbVppRhT9bxVVSUcDpNIJLBYLFpJxvLycvLy8ujq6mL37t00NTX1e43c3FymTJmStvhRVVXa29upr6/H5/P1Wm3ucrnweDw4nU6pFS7EKDJU79kZDyhT/vd//5df/epX7Ny5s8/tJSUl3HnnnXz729/OcMvEoZKAUoihFQgEqKmpIRgMEo1GCQQCWtLy1Gpwh8OByWTCbrdTVlaGw+HodY6dO3fS3t7e73UKCwuZOHEisVisz+slk0l0Ot2grieEGJnGXECZsmXLFtasWUNzczOJRIL8/Hzmzp3L8ccfL596RwkJKIUYeqqq4vf7qaur67fHsLi4mJycnH5fO1VVpbW1lV27dvVKNZQSiUTo6urCZDLh9/sPuUd0MOLxeK9sH5MnTx5R1XyEGKuG6j172J+9M2fOZObMmcPdDCGEGNEURcHpdOJ0OonH40QiERKJBHq9HrPZPKhgTFEU8vPzycvLo7Gxkd27dxONRrXtkUiExsZGotEo4XCYrKwsCgoKsNvtaUFqKs+l3++nsbERt9tNdXU1lZWVg+qpTCaT1NfXpz02ceLEwf8yhBAjTkYX5QghhDhyBoMBu91OdnY2drv9kHv2FEWhqKiIE088kUmTJqHX61FVFZ/PRywWIxgMotfrURSF9vZ2Lbl6z+PtdjtFRUVYrVa8Xi+hUIiamhpZOS7EMUoCSiGEOEbp9XpKS0s58cQTycnJIRaLaUPcNpsNRVGIx+N4vV4aGxvp6upKO15RFNxuN0ajUavYk6puJoQ4tgzLkHc8HueNN97gww8/ZNeuXQQCgYMm2lUUhffeey9DLRRCiGOHyWTCbDbjdrsJBoOYTKZe8zBTw+E2m43c3FyMRiPQ/dqck5OD1+slGo1SV1eH0+kc8Ho6nQ6Px9PrMSHE6JXxgHLFihUsWbKEffv2aY8dLFnvgXVshRBCHD3xeByfz0dXVxfZ2dm4XC7a29uJRCK99g2FQoRCIRwOB06nU+vN1Ov1BAIBfD4f8Xh8wGF4g8HA9OnTh/KWhBAZltGAsqqqinPOOUdLd2GxWJg2bRpOp1M+nQohxDCJRCKoqko0GsVqtWK1WrFYLFruy74SmQcCAYLBINnZ2WRnZ2O1WrXX9lTpRyHEsSOjz/i7776bSCSC2WzmgQce4IYbbsBisWSyCUIIIQ6QmnKUTCa10aDUwhubzUYgEKC9vZ1kMpl2XDKZpL29nUAggKIo2rFSK1yIY09GA8qPPvoIRVH48Y9/zM0335zJSwshhOhHqhKZTqfrNQVJURSys7PJysrC7/fT0dHRa59EIkEoFEJRFHJzc2XESYhjUEaf9akVguecc04mL3tM2bt3Lz/4wQ8oKyvDbreTl5fH/Pnzuf/++wmFQsPdPCHECGQ2m7WKO+FwuM957TqdjtzcXIqLi9NKNEL3PPhYLIaqqjQ3N7Nt2zYCgUCmmi+EGAEyGlCmEtfGYrFMXvaY8dprrzF79mweeOABtm7dqs1/WrNmDXfccQdz585lx44dw91MIcQIYzAYcLlcOBwOrbfxYPt6PB6sViuAFkyazWZsNhsdHR2sXbuWLVu2EA6HM3UbQohhlNGA8sILLwTggw8+yORljwnr16/niiuuoKOjg6ysLP7zP/+TlStX8t5773HjjTcCsG3bNs477zzpORBC9OLxeDCZTFgsFvx+/0ETlJtMJsaNG4fb7dZWdev1erKzs7V9mpubWbVqFdu3b0+ryJNIJNi5c2fa14HzLuPxOMFgkI6ODoLBYJ8Lg4QQI0dGa3l7vV7mzp1LJBJh9erVUmrrKDrllFP48MMPMRgMfPDBB3zhC19I237//fdzxx13AHDXXXdx9913H7VrSy1vIUY/VVVZs2YNra2tNDY2YrVacbvdA6ZsU1VVq5KTnZ1NLBajoKCgz2N0Oh0lJSWMHz8eVVVZuXJl2vaTTjoJo9FIe3s79fX1/dYr93g8OJ1OSSUnxGEaqvfsjAaUABs3buT8888nEonws5/9jMsvv5ycnJxMNmHMWbVqFSeeeCIA3/rWt3jkkUd67ZNMJpk1axaff/45TqeT5uZmLTHxkZKAUoixIRAIUFVVRSAQwOv1YjQaycnJ0armpPSs5R2LxXC73TgcDioqKujo6GDfvn399igaDAYKCwvZt29f2uKd2bNns3PnToLBINFolEAgQDQaJZlMotPpMJlMOBwOTCYTdrudsrKyQdUNF0KkGzMBJcCePXs48cQT8fl82qdOm8024DGKorBz584MtXB0ufPOO/nFL34BwKeffqoFlwe67777+Ld/+zcA3n77bb785S8fletLQCnE2NHa2kp1dbU2BztVitFqtWqFJsLhMIlEAovFQm5uLjabjfLycvLy8oDuOZX79u2jtra2z6HzRCKhVdRxOBx0dXVhtVqJRCKHfU0hxOAM1Xt2xjPPvvDCC3zjG98gEAigqqq2KvBgZHijfx999BEAdrudE044od/9Tj31VO37jz/++KgFlD1Fo1FtrpTRaOz379ZzPhV091r0l2okNeF/tOwbj8fT8vXp9XotLcto2Fen0/WblDqRSKTNdRvp+yaTybSeMkVR+u2ZH4n7Qvdcxb6kVlYf7r79PT9zc3MpLy9n69atGAwGotEowWBQew4oioLNZsPhcGA0GrFYLMyYMYOsrCyi0ShGoxGj0ciUKVMoLi5m7969NDY2oqpqWr5Li8VCJBIhGo0SiUTIzs6mtbUVo9GI2+3WVp6n5OXlEQ6H8fv9NDY24na7qa6uprKyEovFMuzPe3mN6DYSnvfyGtHtUF4jjoaMBpSffPIJV155pfaHLi0tZfbs2VIp5wh9/vnnAEydOnXA6hRlZWW9jhmM2traAbc3NDRo369du1Yrqzl//nzsdnufx3z66adpL1DHH3982mT+nlatWpX2pKisrOy3VvDatWu19FQAFRUV5Ofn97nv+vXr01azzpw5k4KCgj733bhxY9piprKyMgoLC/vcd/PmzbS3t2s/T5s2jeLi4j733bJlC62trdrPkydPZsKECX3uu3XrVrxer/bzxIkT+52HvGPHDhobG7Wfx48fz5QpU/rcd9euXdTX12s/ezyefsvi7dmzh/3792s/FxYWpv2/6mn//v3s2bNH+9ntdlNeXt7nvnV1dezatUv7OS8vj9mzZ/e5b2NjI9u3b9d+djqdVFZW9rlvc3MzNTU12s8Oh6PfD10+n48tW7ZoP9tsNhYsWNDnvm1tbWzatEn72WKxsHDhwj737ejooKqqSvvZaDSyaNGiPvft7Oxk3bp12s86nY5TTjmlz31DoRCrV69Oe+y0007rc99IJMKnn36a9tgXv/jFPgOIWCzGxo0btTejQCCAy+VKe41OjSwVFhayceNGNm7cqG1buHChVrAiFWyOHz+ePXv2sGrVqrRrFRUV4fP5CIfDtLa2kpOTQ2FhITqdjr1796YFaEVFRVqida/Xi9frRa/XU1NTQyQSSXuTldeIbvIa0U1eI/6pv9eIoyGjAeXPfvYzEokEOTk5PPXUU5x77rmZvPyY1NXVhc/nAzhot3Vubi52u51gMJj2hD+YVNe4EOLYoSgKFosFi8XCggULtB5GvV6P2WzGYDCQSCQGNXpks9mYOXMmtbW1tLW1aamEIpEIsVhMC/ASiQQNDQ3k5uYO2C63201DQwNtbW2YzeaD1g4XQgy9jHYLrlmzBkVRuOeeeySYPEp6fiI+MNlwX1I9hp2dnUPWJiHE2GIwGLDb7WRnZ2O32w87eEulGiosLMRisdDZ2UkikSAej2tD3LFYjObmZgKBQK9h5BRFUcjJyaGrq4toNEpHR8eR3qIQ4ghl9CNdaujg5JNPzuRlx7SeQzeDmRthNpsBDinZ8MF6MxsaGvrt9hdCiANZLBbcbjehUIhgMNjnnLFEIkEwGESv1/eaIwndvZ56vZ5AIEAoFJKpU0IMs4yu8p49ezbV1dWsWLFCgsqjxOv1anN6rrjiCv76178OuP+4ceNobm5m1qxZafM7jkTPFWM7d+7Uht5lUU63kTCJXibcdxsJk+hH+qKcodoX0p/3wWCQqqoqGhoatMU+Pbf3/D8JaKu7LRaLdn6fz6flvjz++OO1ERh5jRjafUf6815eI7r19xoxJlZ5X3zxxWzevJm3335bAsqjpGcetsEMYweDQWBww+OHw2QyDaqn9FBWmh1KvsyRsO+hDAeOtn0HetMZifum8heOxX1TtbdHy76Q/rzv6urSUgJZLBby8vIIhUK0t7cTi8V6BWPRaJSmpiYsFgs5OTlaYKmqqvZ/YjBtkdeIod13JDzv5TWi26E+P49URscHfvCDHzBt2jQefPBB1qxZk8lLj1kWi0VboXiw1dhtbW1aQCkLbYQQwyWVh7K9vZ2uri46OztRVRW73Y7H4yE/P7/fIKarq4umpiaampqIRCLa44MNIIQQQyOjAaXD4eC9995j1qxZnHLKKfz4xz9m48aNafMAxaGbOXMm0J0GYqB6tz1TIxx33HFD3i4hhOhLajV3e3s7kUiE9vZ2bUhVURQcDgfFxcUDBpbhcFgr+9jV1ZXRnhghRG8ZDSj1ej2lpaWsWrWKrq4u7rvvPubOnYvdbte6qPv7kpQQ/UtNHwgGg6xdu7bf/VasWKF931+OKyGEyASdTofNZsNsNmulHHs6WGDZM9F6R0cHmzdvpq2trc9V4UKIoZfRgDJVGSf1hO/582C+RN8uvPBC7fvHHnusz32SySRPPPEE0J3k9fTTT89E04QQol8Oh0PrMOjo6Og3RdCBgaWqqkQiEQwGA3q9nuzsbPx+Pxs2bKCqqkoCSyGGQUa7/e66665MXu6YsWDBAr74xS/y4Ycf8qc//Ynrr7+eL3zhC2n7/OY3v9Gq43zve987pAnkQghxNOl0Otxut7YK1WQyEYlE8Hq9uN3uPleJpwJLu93O/v37UVUVq9WK0WjU0qEBWmCZk5PDxIkTcTqdUrpXiAzIaNogMXTWr1/PokWLCIfDZGVlceedd3L66acTDof561//yqOPPgrA9OnTWbNmTdrq8CM1VCkIhBBjXyAQoKqqikAggNfrxWg0kpOTg81mSwsEU8Pifr+fWCyGy+UC6Le8a8pgA8t4PE4kEulVDUiIsWao3rMloBxDXnvtNa655pp+q0ZMnz6dN954g6lTpx7V60pAKYQ4Eq2trVRXVxMKhWhra6Orqwu9Xo/VatVSA4XDYRKJhJaP0mazUV5ejtPppLGxkb1796at+j5QX4Glqqq0t7dTX1+Pz+dLGyZP1Sv3eDzSyynGFAkoxaDs3buX//7v/+aNN96gtrYWk8nE1KlTueyyy7jllluw2WxH/ZoSUAohjlQgEKCmpoZgMEg0GiUQCBCNRrWFNyaTCYfDgclkwm63U1ZWljbSkkwmDymw1Ov1bN26tdf1ksmklutvoOsJMVpJQClGLAkohRBHg6qq+P1+6urq+u0xLC4uJicnp98ew8EEluFwmI6ODsxmM+FwmEgkMuge0by8vCG5dyEyZVRVynnxxRe5+OKLh+LUANTX17Nv3z4WLlw4ZNcQQgiRWYqi4HQ6cTqdhz2nUafT4fF4KCws7DOwjEQiNDc3E41GCQaDmM1mCgoKyM3NTavO03POZmNjI263m+rqaiorK6WnUog+DEnaoEsvvZTKykqef/75o3re/fv3853vfIcpU6bwzjvvHNVzCyGEGDkMBgN2u53s7GzsdvshL5BJBZYnnngi06dP1/Jdpup/B4NBjEYjFouFQCBAY2MjwWBQ6xVVFAW73U5RURFWq1VLol5TUyMpiYTow5D0UE6ZMoWNGzdyxRVXMGHCBK666iquuuoqysvLD/lcwWCQl156iaeffpp3332XeDyOwWBgypQpQ9ByIYQQQy1VerGn4uLiISmf2LPHcvv27dTV1WmLfnquJI9Go2mrzO12O4qioCgKbrebhoYG2traMJvN+P1+nE7nUW+rEKPZkASUW7Zs4cEHH+RXv/oVe/fu5b777uO+++5j2rRpLFy4kPnz5zN37lxtmCE3N5dwOExrayttbW1s27aN1atXs2rVKq2qTuoT4cUXX8zPf/5zpk+fPhRNF0IIMcQSiQS7du1Ke6ywsHBI63HrdDri8Thut5tQKITBYOhzHmYsFsPn8+H3+9MCy5ycHLxeL9FolLq6OgkohTjAkASURqORH/7wh9x888089NBD/P73v2f//v1s27aN7du38+STTw7qPKkg0mw2c/HFF/O9732PBQsWDEWThRBCjGHxeByfz0dnZydWq5Xi4uK0vJYHSgWW7e3tWmCp1+sJBAL4fD5ttEwI0W1ISy9mZWVxxx13sHv3bt566y1uuOEGSktLB1Vm0Ww2c+qpp/LAAw9QV1fHU089JcGkEEKIwxKJRFBVlWg0itVqRafTkZWVhcfjwe12YzKZ+jwuHo/T0tJCfX192nkGSk0kxLEoIx+vdDodZ599NmeffTYAdXV1rFy5ktraWrxeL62trVgsFtxuN263m4qKCubNmyflAYUQYgzS6XS90u/0XGE9FBKJBNCdVqjnUHdq8Y3NZtN6LKPRaK/j4/E4oVAIVVXJysoiGo0etEqPEMeSYemvLy4u5rLLLhuOSwshhBhmBoOB2bNnZ/SaqfmZOp2uz1XaPQPLcDiM3+/vsxcymUzS2tpKVVUVU6ZMGbLFREKMNjIBRAghxJhnNpu1ijupnsa+FuUoioLNZsNqtdLV1UV7e7s2zB2LxbSRM1VV2bVrF/v27aOkpITi4mIZVRPHNAkohRBCjHkGgwGXy0UkEiEQCBAKhQYcslYUBavVisViIRKJ0NTUpM3vt9ls2hB9PB5nz5497N+/H4/HQ0lJCWazOVO3JcSIMbSTVoQQQogRwuPxYDKZsFgs+P3+QSUoT/VqGo1G8vPzycrKIjs7u9d+iUSC/fv389lnn7Ft2zbC4fBQ3IIQI5YElEIIIY4JTqcTu91Obm4usVgMr9d70KCyZ3WdgoICJk+ezBe+8AXy8/P73D+ZTFJfX8+qVav4/PPPCQaDfe4Xj8cJBoN0dHQQDAaJx+NHfH9CDCcZ8hZCCHFMUBSFsrIyqqqqcLvdeL1eGhoayMnJSauaA+m1vGOxGG63G5vNRllZGQ6Hg4qKCjo7O9m3bx/Nzc29rqWqKk1NTTQ1NeFyuZgwYQIOh4P29nbq6+vx+XxpwayiKLhcLjweD06ns8/5nUKMZBJQCiGEyKhEIkFjY2PaY0NdKSfF4XBQXl5OdXU1er2etrY2vF4ver0eq9WKoiioqko4HCaRSGCxWCgsLMRms1FeXo7D4dDOlZWVxcyZM5k0aRL79u2jsbGxzx5Pn89HXV0doVAIi8WCTqejs7OTaDRKMplEp9NhMpmIRCJ4vV7sdrsWuAoxWkhAKYQQIqMSiQTbt29Pe8ztdmcs/U5eXh6VlZXU1NRgNpuJRqMEAgGi0ai2+ttms+FwODCZTAcN8KxWKzNmzGDixIns37+f+vp6ksmktj0cDtPc3EwsFtNKCdtsNrKzs7U0RqFQiEAggMViITc3l6qqKsrLy3vl6xRipJKAUgghxDHH4XAwb948/H4/dXV1/Q5BFxcXk5OTM6ghaLPZzNSpUyktLaW2tpa6ujqCwSDNzc1Eo1GCwSB6vR6z2YxOpyMajWplHQFtiL2xsRG32011dTWVlZXSUylGBQkohRBCHJMURcHpdOJ0OonH40QiERKJhBb0HW6tbqPRyKRJkygpKeH9998nmUwSDAYxGo1pczV71gvPzs4mKysLm82G1+vVhuFramqYN2+ezKkUI96QBZSbN2+mvLxcngRCCCHS6HQ6nE5nr8eGk8FgOOwAsj+dnZ1amiJVVTEajVoJyJ7i8Titra20t7fjcDjIy8ujubmZtrY2zGYzfr+/1+9LiJFmyALK2bNnY7fbtfJUQgghBHQHb5WVlcPdjCFXX19PNBolEokwbtw4bDYbwWBQWzl+oGQyid/vp6OjA71eTzgcJhqNUldXJwGlGPGGdMg7FAr1WQtVCCGEGMvi8Tg+n49AIIBer9eGurOysrDb7QPWC0+Veezs7GT//v0kk0mmT58upR3FiDZsYwy7du3ikksu4Ze//OVwNUEIIYQYEqn639FoVEtHlJJaRV5YWMi4ceOwWq29jlcUBaPRSCgUorGxkVWrVtHY2Ji2elyIkWTYAspwOMxLL73EnXfeOVxNEEIIIYZEaq5kMpnsdy1Bql74uHHj8Hg8ZGVl9btvZ2cnNTU1fPbZZ+zbt6/PIXPx/9u78+imqn0P4N80aZsmaZoO6UALLYNQQCYpylBlEERYOCtPQAUfIIK6FLnivV4F0YsId4FenzgDgtwLwrpeKYIyVBAtVIQWBJS5xbZ0SAc6JG3TJPv9wct5DU3StOmQwvezVtcKOfvss8/ZKf1ln7P3j9oTZ3kTERG1MPuamvZ1JhsTEBCAiIgIhIaGoqKiApWVlQ7b7YFmbW0tLl68iOzsbERHRyMuLg4qlarlT4CoiRhQEhERtbDAwEDIZDIEBATAZDJJC6Y3Ri6XIzQ0FFqtFllZWdIt7mtnoNtzhl++fBnh4eGIi4trNGVjSy6NRHQtfpKIiKhN2Wy2BvmvIyMj233poJakUCgQERGB2tpaVFZWwmQySQuYe6KmpgYKhQIxMTEICQmRlg9ypqSkBCUlJVCr1YiLi0NUVJR0LYUQzB9ObYIBJRERtSmLxYLTp087vBcWFoaAgIB2alHr6NSpEwwGA5RKJcrLyx0WNXfHvnyQUqlEYGAgEhMTodPpUFlZiZycHBgMBqe30Y1GI86cOYOLFy+iU6dO0Gq1uHjxIoxGo0N6SeYPp9bQ6gElv/EQEdGNSKfTQa1WIzQ0FAUFBTAYDNDr9W7/LgohUFxcjLq6OkRHR0OtViMkJATA1XSRffr0QU1NDfLy8pCfnw+LxdKgjrq6Opw+fRoGg0FaTN1isUAul0szzpk/nFpaqweUY8aMQf/+/dGvXz/pp0+fPq19WCIionYlk8mQmJiIY8eOQa/Xw2AwID8/HyEhIQ1GK+0Bnn3Rc71eD5VKhcTExAYBqFKpRPfu3ZGQkICCggLk5uaiurpa2l5bWyvlDy8tLYVcLodWq0VYWJjDcesfk/nDyVutGlAKIVBYWIi9e/di79690vt+fn6IiYmR/r1//34MGDAAoaGhrdkcIiLyATKZrEHAcr3ezQoODkbfvn1x6tQpyOVylJWVSXm6648WVldXw2q1QqlUIjo6GiqVCn379nUb2MnlcsTGxqJTp04oLS1Fbm4uSktLpRHO+vnDhRAwGAxQKBQIDg6GRqOBXC6HWq1m/nBqEa0WUH744Yc4duwYjh07hhMnTsBkMknbrFYr8vLypA/rnXfeCQDo3LkzBg4ciIEDB2LQoEEYOHAg4uPjW6uJRETUDvz9/TF48OD2bkabCQsLw8CBA3H69GkEBgY6PM9on/2tUqkQHByMgICAJj/PKJPJEB4ejvDwcOTl5aGkpARXrlxxyNBjZ7FYUFZWhitXrkCtVkOr1SIgIAB6vR75+fnMH07NJhOeLJDlJSEEzp49KwWY9p/CwsKGDbrmG5FOp8OAAQMwaNAgrFy5srWbSs2Qm5uLzp07AwBycnIQFxfXzi0iIvI9QgiUl5cjLy/P5Yzr2NhYhISENHt08NSpU8jLy0Nubi5UKhXq6uqkRdZdCQwMhFarlZ7f7NSpE2JjY9G3b99mtYF8W2v9zW6TgNKVwsLCBkHmuXPnnKaWkslkjf5SUPtgQElE1DStsSakxWJBWloaiouLYTKZpP+LjUYjKioqYDab3e4vl8tRXV2N0NBQREZGYsSIEVyn8jrUWn+z2/WTEhUVhfHjx2P8+PHSe9XV1fj1118dgswTJ044PHBMRETUkSkUihYP1lzlD9doNFCr1TCbzaioqJAWWr+WfTa4/e5hcXExoqKi+CwlecTnvnoEBQXhtttuw2233Sa9l5GRgb/+9a/t2CoiIiLf5i5/uEwmQ2BgIPR6PSwWC6qqqlBZWen0zp8QAkajEcePH0d4eDhiY2MRGRkppZMkcsbnAkq7/Px8bNy4EV988QVOnTrV3s0hIiLyaZ7mD1coFNDpdAgJCYHJZEJFRQVqa2sblJPJZKiqqsKZM2dw4cIFREVFITY2lrnDySmfCiirq6vx1VdfYcOGDfj++++lZyk9zYFKRES+z2azobi42OG9iIiI6yr1GntAwgAAL3FJREFUYntoav5wmUwGtVoNtVqN2tpaVFRUoKKiQroVX/+WvMViQV5eHvLy8qDT6dCpU6dG+4y5w28sPtGz+/btw4YNG/DVV1+hqqoKAKRvVwwkiYiuLxaLBb/99pvDe8OHD7/uUi+2NW/yhwcGBkKlUkGj0UCr1UIul7sMFq9cuYIrV64gICAAMTExiImJgVKpBMDc4TeydgsoT58+jQ0bNuCf//wncnNzAfx/EKlQKDB+/HhMmzYN1dXVmDlzZns1k4iIqMPwNn+4SqVCREQEBgwYAJvNhry8PJSWljrdx2w249KlS7h06RIiIiKg1WpRUFAAk8nE3OE3oDYNKEtKSrBp0yZs2LABR48eBQCHby/Dhw/HtGnTMHnyZISHhwMAvvzyy7ZsIhERUYfVUvnD7SOI4eHhqK6uRn5+PvLz81FXV+e0jpycHBQVFUEIIf34+/szd/gNpNUDyrq6Omzfvh0bNmzAd999J30Y7YFk7969MW3aNEydOhUJCQmt3RwiImpn9sww175H3muN/OFBQUHo1q0bEhISYDAYcPnyZZSXl0vb6+cONxqNkMvlUCqVCA4OlrL/1A8qmTv8+tRqAWV6ejo2bNiALVu2oKysDMD/B5GxsbF49NFHMW3aNAwcOLC1mkBERD7I398ft956a3s347rVWvnD/fz8EBUVhaioKFRVVeHy5csoKChwmjtcJpPBaDTCaDQiICAAwcHB0gQg5g6/PrVaQDl8+HDpQwsAISEhePjhhzFt2jSMHDmSHxwiIqJW0tr5wzUaDXr27InQ0FBcuXIFFRUVTnOHA1eftSwpKUFZWRnUajWCg4OZO/w61Oq3vGUyGWbOnIl3332Xa1cRERG1keDgYCQlJbVq/vCioiIolUoolUqEhITAarW6zMRjs9lQWVkpPUcZEBAgBbn25Yio42rVgNL+LWjNmjXYvn07HnnkEUyZMgXDhg1rzcMSERERrgaNOp0OOp2uxdeFtFgsKC4uRmVlJRQKBUJDQyGTyWC1WqVMPBaLxem+NTU1EEKgqqoK+fn58PPzQ69evbhOZQfWaqvI7t+/HzNmzIBGo4EQAoWFhVi9ejWSk5PRvXt3vPbaa/j9999b6/BERERUj0KhgFqthlarhVqt9jp4c5U7XC6XIyQkRErZGBQU5HR/mUwGuVyOiooK5ObmIjMzs8EoKnUcrRZQ3nHHHVi7di0KCwvxz3/+E+PHj5fSQWVlZeGtt97CzTffjEGDBmHlypXIy8trraYQERFRC3OXOxz4/9n8UVFRiIuLQ0hIiNt84KWlpTh58iQOHTqErKws1NTUNLlNFosFRqMRFRUVMBqNLkdIqeW1+tiyUqnElClTMGXKFBQUFGDjxo3YuHEjfv31VwDAr7/+ioULF+Lll19GcnIyHnvsMTz88MN8loKI6Dpls9mk1T/sQkNDmXqxg/E0dzgA6Za4TqeT1qO8NmC0B6X1F0wPCwtDTEwMwsPDXX4+mJ3HN8hEO40tHz9+HOvXr8emTZtQWFh4tTH/19H+/v6YMGECpkyZAqPRiJkzZ0rPZZDvyc3NRefOnQFcXdw2Li6unVtERL7MbDbj4MGDDu8x9WLHY7FYkJaWhuLiYphMJsTFxTUpYKutrUV2djaEEFAqlejSpYvLoNHf3x/R0dGIiYlxmOBbWVmJ06dPw2g0uszO09yZ7Ner1vqb3W4BpZ3VasWuXbuwYcMGpKSkSN9Y7B9KmUwmDaczoPRNDCiJqCkYUF4/Tp06hby8PFy+fBl6vd7j3OEAYDQaYTAYEB0dDY1GA5VKhYqKikb3CwkJQUxMjLSGpclkQllZGWpqatyutRkaGiqttXkjZ+dprb/Z7T6dSi6XY+LEiZg4cSIqKirw5Zdf4osvvkBaWpqUvomIiIh8j7e5w+1LDt18883Q6XTSgulFRUUun38sLy9HUVERioqKIJfLUV1djaCgICnTj6tsQMzO07p86oEVrVaL2bNn48CBA7hw4QIWL16M7t27t3eziIioBclkMimQsP/w2baOqX7u8Lq6OhgMhkYHgurnDg8NDYVarUZISAiA/18wfdiwYUhMTJTed7a/PeWj/c6mzWZrcGyZTAa1Wo2YmBgEBQXBYDDAZDLh9OnTHLBqYe1+y9sTaWlp+OKLL/DRRx+1d1PICd7yJiK6cVVWVuLYsWOorKyEwWCAv7+/x7nDg4ODGx0tNJlMyM/PR0FBAerq6lBdXY3CwkJUVVVBCAGNRuPwmJxarYZGo0FgYGCD49vXvIyOjsbAgQNvyAnA1+0tb0+MGDECI0aMaO9mEBER0TVaK3e4nUqlQvfu3dG1a1cUFxfjyJEjsFqtsFgsToPWqqoqVFVVwd/fHxqNRlpzUyaTISQkBAaDgdl5WkGHCCiJiIjId7V27nDg6vJEYWFhUj5wq9WKoKAglxN26+rqUFZWhrKyMgQFBUGj0SAoKAhyuRyVlZUoLi6GxWJhdp4WwqtIREREXmuL3OH27DxWqxVhYWEIDw9HdXU1qqqqYDKZXO5XXV2N6upq+Pn5OeQbr62tZUDZQngViYiIqEW0Zu5woGF2HvvIp0ql8iiHuM1mk269y+Vy5ObmomvXrlyyqgUwoCQiIqIWp1AoWnz0z112HnsOca1Wi9raWlRVVcFoNLqczW3PyFNQUIDQ0FBERUUhIiLCbXpIco0BJRERtSmbzdZgAWutVsvUi9Qo+8ztgIAA6bb1tbfO6y9LFRYWBqPRCKPRiJqaGgghUFdXB39/fwBXg14hBEpLS1FaWgq5XI6IiAhERUUhNDSUy1k1AQNKIiJqUxaLBceOHXN4j5lyyBMKhQIRERGora1FZWUlTCaT2+w8fn5+CA4ORnBwMOrq6lBcXIzKykoEBgZCpVI1+BJjtVpRWFiIwsJCBAQEIDIyElFRUQ5LE5FzDCiJiIiow2hudh65XA4hBPR6PXQ6HfR6PUwmE2w2m9PyZrMZubm5yM3NhUqlQlRUFKKioqBUKlv6lK4LDCiJiIiow6ifnaegoAAGgwF6vd5tUFk/O090dDTCwsIwePBg2Gw2FBcXo7CwEKWlpS73N5lMyMrKQlZWFnQ6HaKioqDX6zlDvB5eCSIialMymUx6hq3+e0SekMlkSExMxLFjx6DX62EwGJCfn+9xdh6VSoXExETIZDLI5XJp5NFsNku3u6uqqlwe/8qVK7hy5QrOnTsHvV7P5y3/DwNKIiJqU/7+/sx+Rl5pjew8AQEB6Ny5Mzp37gyj0YjCwkKHXOHXstlsDs9bRkVFITIy8oZ93pIBJREREXU4rZmdR61Wo1u3bujatSsqKiqk4NLV+pZmsxk5OTnIyclBUFAQIiMjERkZ6XbC0PWGASURERF1SK2dncee/zskJAQ9evRASUkJCgoKUFpa6nJ9y+rqaly6dAmXLl2CWq2WgsugoCCvztXXMaAkIiKiDqu1s/PY+fn5Qa/XQ6/Xw2w2o6ioCAUFBW6ftzQajdJkHq1Wi8jISOj1egQGBrZIm3wJA0oiIiK6LrRGdh5nAgICEBcXh7i4OFRVVaGoqMjt85YAUFFRgYqKCpw/f16aKR4REdFgglpHxYCSiIiIqJk0Gg00Gg26du2KyspKFBUVwWAwoLa21uU+9pniZ8+eRVhYGCIjIzt82kcGlERE1KZsNluD24QajYapF6lDk8lk0Gq10Gq16N69O8rLy6Xgsq6uzuk+QgiUlJSgpKQEfn5+iIiIQGRkJMLCwjrc7wMDSiIialMWiwUZGRkO7zH1Il1P6j/X2aNHD5SVlaGoqAjFxcWwWq1O97HZbNKtc4VCAb1ej8jISOh0ug6xDBEDSiIiIqJW4ufnh/DwcISHh8NqtaK0tFTKzOMq7aPFYkF+fj7y8/OlnOLx8fE+/bwlA0oiIiKiNiCXy6WZ4haLBcXFxSgqKkJZWZnLZYjMZjMKCgrQrVu3Nm5t0zCgJCKiNtfRng8jamkKhQLR0dGIjo6G2WyGwWBAUVERysvLG5SNiIjw+d8ZBpRERNSmAgICcMcdd7R3M4h8RkBAAGJjYxEbG4uamhrpWUr75LXIyMh2bmHjGFASERER+QilUokuXbqgS5cuMJlMMBgMCA0Nbe9mNYoBJREREZEPUqlUiI+Pb+9meMS3b8gTERERkc9jQElEREREXmFASURERERe4TOURETUpoQQMJlMDu+pVKoOkQ2EiJxjQElERG2qrq4Ov/zyi8N7TL1I1LHxljcREREReYUjlOQ1i8Uivc7Pz2/HlhBRR2DPClJfbm4uRyiJ2kD9v9P1/357iwElea3+H4Zbb721HVtCREREnjIYDEhISGiRunjLm4iIiIi8IhNCiPZuBHVsNTU1OHHiBABAr9dDoeDAt6fy8/OlUd3Dhw8jJiamnVtEzcF+7PjYh9cH9mPjLBaLdGexX79+UCqVLVIv//KT15RKJYYMGdLezejwYmJiEBcX197NIC+xHzs+9uH1gf3oWkvd5q6Pt7yJiIiIyCsMKImIiIjIKwwoiYiIiMgrDCiJiIiIyCsMKImIiIjIKwwoiYiIiMgrDCiJiIiIyCtc2JyIiIiIvMIRSiIiIiLyCgNKIiIiIvIKA0oiIiIi8goDSiIiIiLyCgNKIiIiIvIKA0oiIiIi8goDSiIiIiLyCgNKIiIiIvIKA0oiIiIi8goDSiIiIiLyCgNKoiYym8347LPPMH78eMTExCAwMBAajQa9evXCk08+iYMHD3pUz7fffosHHngAcXFxCAwMRFxcHB544AF8++23HrfFYrHgo48+wu233w69Xo+goCB0794dc+bMwalTp5p7ih1SUVERvvnmGyxatAgTJkxAREQEZDIZZDIZZsyY0eT6fKl/iouLsWjRIvTv3x9arRZarRb9+/fHokWLUFJS0uRz82Ut0Y8mkwlfffUV5s6diyFDhiA0NBT+/v4IDw/HsGHD8Prrr6OgoMDjNplMJqxYsQJDhgxBWFgY1Go1EhMTsWDBAly6dMnjei5duoQFCxYgMTERarUaYWFhGDJkCP7+97/DZDJ5XI+va+nfxfpMJhO6desm1ZeQkODxfuzDViaIyGPZ2dmib9++AoDbn+eee07YbDandVitVjFz5ky3+8+aNUtYrVa3bTEYDGLIkCEu6wgMDBSffvppa1wGn+Tuek6fPt3jenytf9LT00V0dLTLemJiYsTPP//s8fn5Om/78fjx40Kj0TT6O6rVasXmzZsbre/cuXPipptuclvP9u3bG60nJSVFaLVal/X07NlTnDt3zpNL5PNa6nfRmQULFjjUFx8f3+g+7MO2wYCSyENms9khmOzfv7/4/PPPxaFDh8Tu3bvFokWLhFqtlrYvW7bMaT1//vOfpTKDBg0SmzZtEocPHxabNm0SgwYNkrb95S9/cdkWi8UikpOTpbIPPvig+Pbbb8XPP/8s3nvvPREZGSkACD8/P7Fz587WuiQ+pf5/7F26dBF33XVXs/6I+VL//PHHH0Kv1wsAQqFQiIULF4oDBw6IAwcOiIULFwqFQiEAiMjISJGTk9OUy+WzvO3HH3/8USo/YsQIsWzZMrFnzx6RkZEhdu3aJebMmSP8/PwEACGXy91e/4qKCtGzZ0+pvtmzZ4vU1FRx8OBBsXTpUilwValUIjMz02U9GRkZIigoSAAQGo1GLF26VBw8eFCkpqaK2bNnOwQkFRUVzbhqvqWlfhevlZGRIeRyuVAqlSI4ONijgJJ92HYYUBJ5aOvWrdJ/GsOGDRMWi6VBmSNHjgh/f38BQOh0OlFXV+ew/cyZM1IQkJSUJEwmk8N2o9EokpKSpADC1bfdNWvWSG2ZN29eg+3nzp2Tvkn36NGjQTuuR4sWLRLbt28XBQUFQgghsrKymvxHzNf65/HHH5fq2bJlS4PtX375ZYuN/PgKb/sxLS1NTJ48WZw6dcplma+//lrIZDIBQHTv3t3l3YTXXntNOvaKFSucHsv+eRk5cqTL491+++3SZ+bgwYMNtq9YsUI6zuLFixs9R1/XEr+L17JYLGLw4MECgHjjjTdEfHy8RwEl+7DtMKAk8tD8+fOl/zBSUlJclnvggQekcr/++qvDtrlz50rbDh065HT/Q4cOuQ1GhBCid+/eAoAICwsTRqPRaZlly5a5DUaud835I+ZL/ZOfny+NpI0fP95lm8ePHy+Ndubn53twlh1LSwQjzjz00ENSvUePHm2w3Ww2i5CQEAFA9O7d2+UjDnPmzJHqOXz4cIPtP//8s7R9zpw5TuuwWq3SZ0an0wmz2ezdyfmYlujDlStXCgCiV69eora21qOAkn3Ytjgph8hDZrNZet2tWzeX5bp37+50HyEEtm3bBgBITEzE0KFDne4/dOhQ9OrVCwCwbds2CCEctp89exa///47AGDy5MlQqVRO66n/8Pt//vMfl+2lq3ytf1JSUmCz2QAATz75pMt22+ux2WxISUlxWY4cjR49Wnp94cKFBtv37duH8vJyAMD06dPh5+f8z2Vj/fj1119Lr131o5+fH5544gkAwJUrV7Bv375G238juXTpEhYtWgQA+OijjxAQEODRfuzDtsWAkshD9iACAC5evOiynP2Pk0wmw0033SS9n5WVhcuXLwMARo4c6fZY9u15eXnIzs522PbTTz81KOdMdHQ0evbsCQBIS0tzezzyvf7xtJ7629jPnqutrZVey+XyBts9vf5JSUnSlwZ3/ahWqzF48GCX9bAfXZs3bx6MRiMef/xxjBo1yuP92IdtiwElkYemTJkCrVYLAFi+fDmsVmuDMpmZmdixYwcAYOrUqVJ5APjtt9+k14mJiW6PVX+7fbTLm3pycnJgNBrdlr3R+Vr/2OsJCQlBdHS0yzpiYmKkz9m1bSHXfvjhB+l17969G2z3tB8VCgV69OgBwPn1t7/Xo0cPKBQKl/W4+0zdyDZv3oydO3ciNDQUK1eubNK+7MO2xYCSyEMRERH44osvoFKpkJaWhiFDhmDDhg1IT0/H3r17sWTJEowcORJmsxm33HJLg//8cnNzpddxcXFuj9W5c2fpdU5Ojtf1CCEc9qOGfK1/7P9urI769VzbFnLu+PHj0he/fv36OQ0o7ddfrVZDp9O5rc9+/Q0Gg8PIZ01NDYqLiwE03o+hoaFQq9UA2I92ZWVleOGFFwAAb7/9NvR6fZP2Zx+2LdehNhE1cO+99+Lo0aNYuXIl1qxZg+nTpztsj4qKwptvvonZs2c3eHausrJSeq3RaNwex/6fEgBUVVW1Sj3kyNf6x15PY3XUr4d93Lja2lrMmjVLusOwdOlSp+Wac/2Bq30QGBjoUEdT6jEajezH//PSSy+hsLAQw4YNw+zZs5u8P/uwbXGEkqgJzGYzNmzY4HQyBgAUFhZi48aN2Lt3b4NtNTU10uvGHiq3/2cGANXV1a1SDznytf6x1+PJBAR7Pezjxj377LM4cuQIgKsTNe655x6n5Zpz/QHHPmjKZ6F+PexH4MCBA1i7di0UCgU++ugjyGSyJtfBPmxbDCiJPGQ0GjF27FgsW7YMpaWlWLhwIX7//XfU1taivLwcu3fvRnJyMo4cOYL7778fq1atcthfqVRKr+vP/nam/i2XoKCgVqmHHPla/9jraayO+vWwj91btmwZPvvsMwDAkCFDsHr1apdlm3P9Acc+aMpnoX49N3o/1tbW4qmnnoIQAs8//zz69+/frHrYh22LASWRh15//XX8+OOPAIA1a9Zg+fLlSExMREBAALRaLcaNG4d9+/Zh9OjREELgpZdewvHjx6X9g4ODpdeN3Q6pP0Hj2tssLVUPOfK1/rHX48mtM3s97GPXPv74Y7zyyisArk6e2Llzp8Ntzms15/oDjn3QlM9C/Xpu9H5cunQpzpw5g86dO2PJkiXNrod92Lb4DCWRB4QQWLt2LQCgZ8+eDZ6dtFMoFHjzzTeRnJwMm82Gzz//HO+88w4Axwe6G5sgU/+B7voTQJzVExER0Wg9MpnMo8kdNzJf65+4uDgUFhZ6NJnKXs+1baGrNm3ahHnz5gEA4uPjsWfPHrf9Aly9/j///DOMRiOuXLnidlKH/frr9XqHW6dKpRLh4eEoKSlptB/LysqkYORG78fly5cDAMaOHYvt27c7LWO/VkajEZs3bwYAREZGYsyYMVIZ9mHb4gglkQcKCwtRWloKABg0aJDbsvXXKTt9+rT0uk+fPk7fd6b+9mtnoDanns6dO7sdjSHf6x97PeXl5SgoKHBZR35+PioqKpy2ha4uEP/EE0/AZrMhJiYGqampHn258rQfLRaLtPass+tvr+f8+fOwWCwu63H3mbrR2G8tr1u3DlOmTHH6Y595XVxcLL33xhtvONTDPmxbDCiJPFB/7TF3/6EAQF1dndP9unbtik6dOgFwXAPPmQMHDgAAYmNjkZCQ4LAtOTlZeu2unoKCApw9exYAMGLECLfHI9/rH0/rqb+N/ewoNTUVkydPhsViQXh4OPbs2eOQycodT6//kSNHpFEpd/1oNBpx9OhRl/WwH1se+7BtMaAk8kBYWJi0ePShQ4fcBpX1/1Pp2rWr9Fomk+G+++4DcPWbbHp6utP909PTpW+69913X4PZjT179pS+/W7ZsgUmk8lpPZ9//rn0+oEHHnDZXrrK1/rn3nvvlVLFrVu3zmW77fX4+fnh3nvvdVnuRnPw4EHcd999qK2tRUhICHbt2oW+fft6vP+oUaMQEhICAFi/fr3TVR2Axvvx/vvvl1676kebzYYNGzYAAHQ6nUNayBuREKLRn/j4eABXH2Gwv7d//36HetiHbawN84YTdWhTpkwRAAQA8frrrzstU1paKvr06SOV27Vrl8P2M2fOCLlcLgCIpKQkYTKZHLabTCaRlJQkAAiFQiHOnj3r9Dhr1qyRjvHMM8802H7+/Hmh1WoFANGjRw9RV1fXzLPuuLKysqRrNH36dI/28bX+efzxx6V6tm7d2mD7li1bmnyOHU1z+jEzM1PodDoBQKjVavHTTz8169ivvfaadOwVK1Y02H7w4EGhUCgEADFy5EiX9dx+++3SZ+bgwYMNtq9YsUI6zuLFi5vVVl/WnD5sTHx8vAAg4uPj3ZZjH7YdmRAuQnYicnD69GkMHjxYGnG65557MH36dHTr1g01NTVIT0/Hu+++iz/++AMAcOeddzpdj/Ivf/kL3n77bQBXn8d8+eWX0b17d1y4cAHLly9HZmamVO6tt95y2har1YqRI0dK+WIfeughzJ49G6GhoTh8+DDefPNNFBUVwc/PD9988w0mTJjQ4tfD1/z00084f/689O/i4mK89NJLAK7efpo1a5ZD+RkzZjitx5f6JycnB4MHD4bBYIBCocCCBQswadIkAMA333yDlStXwmKxQK/XIyMj47qYeOVtP164cAHDhw9HUVERAOCdd97B2LFj3R4zMjISkZGRDd6vrKxEUlKS9GjCU089hUcffRRBQUHYt28f3nrrLVRVVSEoKAgHDx7EwIEDndafmZmJESNGoLq6GhqNBq+88gpGjx6N6upqbN68GZ988gmAq6PbR44ccZhZ3BG11O+iOwkJCbh06RLi4+ORnZ3tshz7sA21d0RL1JHs2bNHRERESN9EXf2MGTNGlJaWOq3DarWK//7v/3a7/8yZM4XVanXbFoPBIIYMGeKyjsDAQPHpp5+2xmXwSdOnT2+0X+r/uOJr/ZOeni6io6Nd1hMdHS3S09ObfL18lbf9uG7duibtj0ZGlM6dOyduuukml/tqtVqxffv2Rs8rJSVFGpV29tOzZ09x7tw5by6dz2ip30V3PB2hFIJ92FYYUBI1UXFxsVi+fLkYNWqU0Ov1wt/fXwQFBYmuXbuKyZMni6+//lrYbLZG69mxY4e47777RKdOnURAQIDo1KmTuO+++8TOnTs9bktdXZ344IMPRHJysggPDxdKpVJ069ZNzJ49W5w8edKb0+xwWvqPmC/1j8FgEK+++qq4+eabhUajERqNRvTr10+8+uqrori42ON6OgJfCyiFEKKqqkosX75cJCUlCZ1OJ1QqlejVq5eYP3++yM7O9vjcsrOzxfz580XPnj2FSqUSOp1OJCUlieXLlwuj0djUS+WzfC2gFIJ92BZ4y5uIiIiIvMJZ3kRERETkFQaUREREROQVBpRERERE5BUGlERERETkFQaUREREROQVBpRERERE5BUGlERERETkFQaUREREROQVBpRERERE5BUGlERERETkFQaUREREROQVBpRERERE5BUGlERERETkFQaUREREROQVBpRERERE5BUGlERERETkFQaURERELez777+HTCZDVFQUTCZTqxzDZDIhMjISMpkM+/fvb5VjEHmKASURtars7GzIZDKvf4g6CpvNhhdeeAEA8Kc//QkqlapBmYSEBMhkMiQkJDRa34svvij9Htx0003IyckBAKhUKrz44osAgBdeeAFCiBY7B6KmYkBJREReswc8r7/+ens3pd1t3rwZJ06cQEREBObNm9fseoQQeO655/DOO+8AABITE/HDDz+gc+fOUplnnnkGYWFhOH78OLZu3ep124maS9HeDSCi61tsbCxOnDjhcnu/fv0AAElJSVi3bl1bNYuo1SxduhQAMGfOHKjV6mbVIYTA008/jU8++QQA0LdvX6SmpiIqKsqhXHBwMJ566im8/fbb+Nvf/obJkyd713iiZmJASUStyt/fHzfffHOj5dRqtUfliHzZnj178NtvvwEAHnvssWbVYbPZMGvWLOkL1oABA7B3715EREQ4LT916lS8/fbbOHHiBPbv349Ro0Y167hE3uAtbyIiohayZs0aAMAtt9yCxMTEJu9vtVoxffp0KZgcPHgw9u3b5zKYBK6O8ttH+u3HJ2prDCiJyOdlZGTg6aefRq9evaDRaKBWq9GrVy/MnTsXZ8+edbnf559/Lj3bl52dDbPZjFWrViEpKQkhISEICwvDqFGjsGPHDof9KisrsWLFCgwaNAharRY6nQ7jxo1Damqqy2Pt379fOtb+/fths9nw6aefYvjw4QgLC4NarcaAAQOwbNky1NTUeHTeX3/9NR555BF06dIFSqUSOp0OSUlJWLJkCcrKylzuN2PGDIcJH/n5+Xj55ZfRt29fBAcHN5gVXFZWhnXr1uGxxx5Dnz59oNFoEBAQgOjoaIwfPx6ffPIJzGaz02PZJ5fYLVmypMGEqhkzZkjbr+0TV+pP5vr888+9PkfgarC2fv16TJo0CZ06dUJgYCDCw8ORnJyMVatWobq62mV7PFFTU4OUlBQAwEMPPdTk/S0WC6ZNm4aNGzcCAIYOHYrU1FSEhoY2uq/9eF9//bXHny+iFiWIiNoRAAFAjBw5ssE2q9Uq5s+fL2QymVTu2h+FQiE+/vhjp3WvW7dOKnf8+HFx2223uaxn1apVQgghLl26JPr27eu0jEwmExs3bnR6rH379knldu3aJe6++26Xx+rTp4/Iz893eU1KS0vFmDFjXO4PQERGRopDhw453X/69OkCgIiPjxeHDh0SERERDfbft2+fVD4+Pt7tsQCIQYMGOW2zJ/tOnz7daZ9kZWW5vAZZWVlSuXXr1nl9jpcuXRIDBgxw284ePXqIM2fOuGxTY/bv3y/VlZqa6ras/brFx8cLIYQwm83iwQcflPZPTk4WFRUVHh/7u+++k/bdvXt3s8+BqLn4DCUR+aznnnsOH3zwAQDgjjvuwIwZM9CtWzeoVCocP34c7777Lk6dOoU5c+YgOjoa9957r8u6nnrqKRw9ehTz5s3DAw88gNDQUBw7dgyLFi3C5cuX8ac//Qnjxo3DjBkzcPHiRfz5z3/G3XffDbVajbS0NCxevBjl5eWYO3cuxo0bh8jISJfHevXVV/HLL7/grrvuwty5c9G5c2fk5OTggw8+kJ6xu+eee5Ceng65XO6wb21tLcaOHYuMjAzI5XJMnToVEydORNeuXVFXV4cDBw5g1apVKCoqwsSJE5GZmYn4+Hin7aiqqsJDDz2Empoa/PWvf8W4ceOgUqlw4sQJxMTESOWsVituu+02TJo0CYMGDUJUVBTMZjOysrKwceNGfPfdd8jMzMSjjz7aYNRv9+7dMJvN0i3XuXPnNpjZ7MkIW3N5co4lJSVITk5GTk4OAgMDMXv2bIwcORIJCQmoqqrC7t278Y9//APnz5/HhAkTkJGRgZCQkCa35ccffwRwdcb74MGDPd7PbDbjkUcekUY3R48eje3btzdpQs+tt94qvf7hhx8wbtw4j/clahHtHdES0Y0NLkYod+/eLW377LPPnO5bXV0tjeTFx8eLuro6h+31R8NkMpn4z3/+06CO48ePCz8/PwFA6PV6ERgYKNLT0xuU27FjR4PRzPrqj1ACEE899ZTTNs+cOVMqs3r16gbbX3nlFQFA6HQ6ceTIEad1ZGdni5iYGAFATJ06tcF2++gdAKHRaMSxY8ec1mN39uxZt9vXrl0r1bd3716nZezbFy9e7Laulh6h9OQcp06dKn1GLl686LRMRkaGUKvVAoB45ZVX3J6DKxMmTBAARPfu3Rstax+h7NSpk5g4caJ0LuPGjRMmk6lZx+/atasAIO6+++5m7U/kDT5DSUQ+6e233wZw9dmwmTNnOi2jVCrx/vvvAwAuXbqEffv2uaxv8uTJuP/++xu8379/fyQnJwMADAYDXnjhBdx2220Nyk2cOFEaCbSPRLkSFRUlrR14rXfffRd6vR4ApNFXu6qqKqxevRoA8Oabb7oc5YqPj8drr70GANi6dSuMRqPLtixcuBADBgxw296bbrrJ7fYnn3wSAwcOBHD1GT1f4+4cs7Oz8eWXXwIA3n//fXTt2tVpuUGDBuGZZ54BAKfPbHoiNzcXANyOXl/r8uXL2LlzJwBg5MiRSElJQVBQULOObz/uxYsXm7U/kTcYUBKRz6moqJBurT788MNuy/bu3VuaAXvo0CGX5R599FGX2+oHI+7K9e/fH0Djf7AnT57sNDsKAGg0GmmtwFOnTqGgoEDa9sMPP6C8vBxA4+d9xx13AADq6upw9OhRl+WmTZvmtp5rCSFQUFCAs2fP4uTJk9JPbGwsAOD48eNNqq8tuDvHHTt2wGq1QqVSYcKECW7rsV/Ty5cv448//mhyOwwGA4Cm3eKvP6HpxIkTbieZNSYsLAwAHD5TRG2FASUR+ZzMzEzYbDYAwJQpUxpNy1hcXAzA/R/Snj17utym0+maVK6ystJt+4cMGeJ2e/3n3eov+n7kyBHpdUxMjNtzrr9mp6vz1mg06Natm9u22O3YsQOTJk1CSEgIYmJi0KtXL2k5mn79+kkz4e3X2lc0do72a2oymaBQKNxe00mTJkn7NScoKy0tBdC0gLJLly546aWXpP3HjRuH06dPN/nY9Y/rbsSaqLUwoCQin1NUVNSs/Uwmk8ttrkYMAcDPz69J5axWq9t2NHbLs362E3sQArT8edcPlF0RQmDWrFmYNGkSduzY0Wiw7O3SOi2tsXNsjc+SK0qlEkDTr9GKFSvw7LPPArja3rFjxzbrtrX9uP7+/k3el8hbnOVNRD6nfsD28ccfY/jw4R7t15qziZui/m3Mpqh/3hkZGR4HBnFxcU7fv3YGuTNr166VFsMeOHCg9AxpbGwsVCqVVMcTTzyBL774AkIIj9rUVho7R/s1jYiIcPuM7bVcPWvpjl6vR0VFhcOXBE+99957MJlMWLt2LfLy8nDnnXfiwIEDDnm7G2M/ridfJIhaGgNKIvI54eHh0muVStXhUjIWFhZ6vN3+3BvgeN56vd5loNiSPv30UwBAjx49cPDgQZcTQpoTJDlTfzTY/liDMy1129Z+TSsrK9G7d2+Pguzm0uv1uHDhgttF512RyWT49NNPUVNTg3/961/Izs6Wgsro6GiP6rAft0uXLk0+PpG3eMubiHzOwIEDpVG+tLS0dm5N0/3yyy8eb68fLA8aNEh63VbnferUKQDAvffe6zKYFEIgIyOjRY4XHBwsvXYXeHkzOaU++zWtra11eEa1NdjX4rxw4YLbYNkVPz8/rF+/Hg8++CAA4Ny5cxg7dixKSkoa3ddms0m3yfv27dvkYxN5iwElEfkcvV6PoUOHAgD+9a9/SbNnO4qtW7e6fI7OaDRiy5YtAIA+ffo4LDA+duxY6RnO9957r01uL1ssFqldrmzbtg35+flu67E/P1hbW+u2XP1bye4CvE2bNrmtx1P33HOP9OXk3XffbZE6Xbn99tsBXF3+6ffff29WHQqFAps2bZJmpJ86dQp33XWXNPvfld9++w1VVVUA4HTZK6LWxoCSiHzSq6++CuDqEkIPP/wwrly54rJsbW0tVq9e7TM5jAsKCrBgwQKn21588UVposjcuXMdtul0OmlyxsGDBzF//ny3I12FhYX47LPPvGqrfQ3K7du3O72tfeHCBWl9RnfsgfGFCxfclrv55pul2/zvv/++0wB0y5Yt2Lp1a6PH9ESvXr3wyCOPAAA2b96MVatWuS2flZXV7GDWHlACwOHDh5tVBwAEBATgq6++wpgxYwBcfZ727rvvlgJGZ+of76677mr2sYmaiwElEfmkiRMn4vnnnwcAHDhwAL1798aSJUuQmpqKY8eOIS0tDevXr8esWbMQExODZ599Vhpta29JSUn48MMPMWHCBGzbtg0ZGRnYtm0b7r77bnzyyScArt6Kffrppxvs+8Ybb0gjTP/4xz9wyy23YPXq1UhLS8OxY8ewb98+vP/++7j//vvRpUsXfPTRR1619YknngBwde3FYcOGYe3atTh8+DAOHDiA119/HYMHD0ZpaSluueUWt/XYJ06lpKTg448/xsmTJ3H+/HmcP3/eYaa1QqHAnDlzAAAnT57EmDFjsG3bNmRmZuK7777DzJkzMWXKFI8nYnniww8/lJYWWrBgAUaOHIk1a9YgPT0dmZmZ2Lt3L1auXIlx48ahR48e+Pe//92s4yQkJEhrlaampnrVZqVSiZSUFIwYMQIAkJ6ejkmTJrkc+bYfb8CAAc2aUETktXbN00NENzy4SL0ohBA2m00sWbJEKBQKh7SGzn7UanWDlHWepvlbvHixVM4de7q/+Pj4Btvqp17ctWuXuOuuu1y2NTExUeTl5bk8TkVFhXjwwQcbPWcAYvTo0U1q57XMZrPbtgYFBYktW7Y0WmdmZqYIDAx0Wsf06dMdyhqNRjF06FCXxxw1apQ4efKkR6kXPTlHIYTIz88Xt99+u0fX9Mknn/SoTmf+53/+R0oHaTQaXZazp15srP3l5eUiKSlJatv48eNFbW2tQxmj0Siljfz73//e7LYTeYMjlETks2QyGRYtWoSzZ89i4cKFSEpKQlhYGORyOYKDg9GnTx9MmzYN69evR35+frNT1rW0gIAA7Ny5Ex988AGGDh0KnU4HlUqFfv364W9/+xsyMjLQqVMnl/sHBwfj3//+N3788UfMmjULvXr1QnBwMBQKBcLCwjBkyBA888wz2LlzJ/bs2eNVW/39/bFjxw689957SEpKgkqlQlBQEHr06IGnn34aGRkZ0i1jdwYOHIhDhw5hypQp6NKlCwIDA12WValU+P7777F06VL069cPQUFB0Gq1GDJkCN5//33s3bsXarXaq/O6VnR0NA4cOIBvvvkG06ZNQ7du3aBSqeDv7w+9Xo/hw4djwYIF+OGHH7B27dpmH+exxx5DUFAQqqqqkJKS4nW7tVotdu3aJY187tq1C//1X//lMBq/bds2GI1GKJVKPPnkk14fk6g5ZEL42KJiREQd0P79+zF69GgAwL59+zBq1Kj2bRC1m3nz5uHDDz/E2LFjvQ74PTF27FikpqZizpw5Xj8CQdRcHKEkIiJqQYsWLYJarcbevXuRnp7eqsdKT09HamoqAgIC8Morr7TqsYjcYUBJRETUgqKjozF//nwAVydZtaYlS5YAAJ5//nkuaE7tiplyiIiIWtjChQuhUFz9E2symdzmiG8uk8mEoUOHYujQoVIAS9Re+AwlEVEL4DOURHQj4y1vIiIiIvIKRyiJiIiIyCscoSQiIiIirzCgJCIiIiKvMKAkIiIiIq8woCQiIiIirzCgJCIiIiKvMKAkIiIiIq8woCQiIiIirzCgJCIiIiKvMKAkIiIiIq8woCQiIiIirzCgJCIiIiKvMKAkIiIiIq8woCQiIiIirzCgJCIiIiKvMKAkIiIiIq8woCQiIiIirzCgJCIiIiKvMKAkIiIiIq8woCQiIiIir/wve49dlIDbIW0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 680x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfs_rs = dict()\n",
    "dfs_rs['bcc'] = get_free_energy(\n",
    "    'my_freen_runs/switch_bcc_T700_nat16000_nsample100000_spring2.000000/ti_spring.yaml',\n",
    "    'my_freen_runs/rs_bcc_Ti700_Ti1500_nat16000_nswitch40000/ti_rs.csv')\n",
    "dfs_rs['hcp'] = get_free_energy(\n",
    "    'my_freen_runs/switch_hcp_T700_nat12480_nsample100000_spring2.000000/ti_spring.yaml',\n",
    "    'my_freen_runs/rs_hcp_Ti700_Ti1500_nat12480_nswitch40000/ti_rs.csv')\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(3.4, 3.5), dpi=200, nrows=2, sharex=True)\n",
    "\n",
    "for phase in ['bcc', 'hcp']:\n",
    "    ax = axes[0]\n",
    "    kwargs = dict(\n",
    "        color = 'C0' if phase == 'hcp' else 'C1',\n",
    "        alpha=0.5\n",
    "    )\n",
    "    ax.plot(dfs_spring[phase]['T'], dfs_spring[phase].F, 'o', label=phase.upper(), **kwargs)\n",
    "    ax.plot(dfs_rs[phase].temperature, dfs_rs[phase].free_energy, **kwargs)\n",
    "axes[0].legend(frameon=False, handlelength=1)\n",
    "\n",
    "ax = axes[1]\n",
    "kwargs = dict(color = '0.5', alpha=0.5)\n",
    "ax.plot(dfs_spring['bcc']['T'], 1e3 * (dfs_spring['bcc'].F - dfs_spring['hcp'].F),\n",
    "        'o', label='Frenkel-Ladd', **kwargs)\n",
    "ax.plot(dfs_rs['bcc'].temperature,\n",
    "        1e3 * (dfs_rs['bcc'].free_energy - dfs_rs['hcp'].free_energy),\n",
    "        label='reversible scaling', **kwargs)\n",
    "ax.axvline(1136, ymax=0.6, color='0.5', alpha=0.5, ls=':')\n",
    "ax.axhline(0, color='0.5', alpha=0.5, ls=':')\n",
    "\n",
    "axes[0].set_ylabel('$F$ (eV/atom)')\n",
    "axes[1].set_ylabel('$\\Delta F$ (meV/atom)')\n",
    "axes[-1].set_xlabel('Temperature (K)')\n",
    "axes[-1].legend(frameon=False, handlelength=1)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.align_ylabels()\n",
    "fig.subplots_adjust(hspace=0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
